It’s 4:57 PM on a Friday, and a junior analyst noticed something odd. The numbers in this week’s dashboard looked exactly like last week’s. Not just similar. Not kind of close. Identical. The pipeline had been rerunning but the data hadn’t changed for seven days, and nobody had a clue(until the CFO notices on a Monday morning). Of course, maybe there is another story here about the fact that no one noticed that the data was stale, but let’s not get into that. One of the challenges with data pipelines is that they can fail without anyone noticing. Dashboards might only be looked at once a month. Data can “look” right. Pipelines can run without triggering any failure or red flag. And what’s worse is that it can happen in multiple ways. In this article, I wanted to discuss the ways pipelines can fail silently and what you can do about it. If you enjoyed this video, check out some of my other top videos. Common Data Pipeline Patterns You’ll See in the Real World - Types Of Data Pipelines You'll Build https://youtu.be/htAipJ6yYFs What Is BigQuery - Breaking Down What BigQuery Is And Diving Into A Hands On Walkthrough https://youtu.be/pud-vuNE15g If you're looking for help ingesting your data in batch or real time, then you need to check out Estuary - https://bit.ly/4eQC3oQ If you'd like to read up on my updates about the data field, then you can sign up for our newsletter here. https://seattledataguy.substack.com/​​ Or check out my blog https://www.theseattledataguy.com/ And if you want to support the channel, then you can become a paid member of my newsletter https://seattledataguy.substack.com/subscribe Tags: Data engineering projects, Data engineer project ideas, data project sources, data analytics project sources, data project portfolio _____________________________________________________________ Subscribe: https://www.youtube.com/channel/UCmLGJ3VYBcfRaWbP6JLJcpA?sub_confirmation=1 _____________________________________________________________ About me: I have spent my career focused on all forms of data. I have focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. I have also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. I privately consult on data science and engineering problems both solo as well as with a company called Acheron Analytics. I have experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data. *I do participate in affiliate programs, if a link has an "*" by it, then I may receive a small portion of the proceeds at no extra cost to you.

Last week, I noticed that Anthropic was hiring for a partner success manager. The very first line of the description was: Consulting and systems integration firms are racing to build Claude practices. From there, the posting went on to explain how important it is for Anthropic to support consultancies, systems integrators, and implementation partners as they bring Claude into the enterprise. And all I could think was: Wait. If the AI is good enough to replace engineers, why does it need a small army of consultants to help companies use it? On one hand, we keep hearing that AI is going to automate software engineering, data work, analytics, customer support, operations, and almost every other form of knowledge work. It will end work as we know it! And yet, every major AI company seems to be investing in partner ecosystems, systems integrators, implementation teams, enterprise success teams, and consultants in one way or another. I had a similar thought about Salesforce recently. If your AI is so good, why do companies still spend millions of dollars paying consultants to set up Salesforce, customize workflows, clean up CRM data, integrate systems, train teams, and keep the whole thing from becoming a very expensive mess? Shouldn’t AI be able to just… do that? But of course, anyone who has worked inside a real company knows the answer. The hard part is rarely clicking the buttons. The hard part is understanding the business, the process, the edge cases, the incentives, the data, the politics, and the mess that already exists. So why? Why do the LLM labs want to partner with consultancies? My thought? AI is powerful enough to change work, but not simple enough to magically reorganize companies by itself. If you enjoyed this video, check out some of my other top videos. If you enjoyed this video, check out some of my other top videos. Common Data Pipeline Patterns You’ll See in the Real World - Types Of Data Pipelines You'll Build https://youtu.be/htAipJ6yYFs What Is BigQuery - Breaking Down What BigQuery Is And Diving Into A Hands On Walkthrough https://youtu.be/pud-vuNE15g If you're looking for help ingesting your data in batch or real time, then you need to check out Estuary - https://bit.ly/4eQC3oQ If you'd like to read up on my updates about the data field, then you can sign up for our newsletter here. https://seattledataguy.substack.com/​​ Or check out my blog https://www.theseattledataguy.com/ And if you want to support the channel, then you can become a paid member of my newsletter https://seattledataguy.substack.com/subscribe Tags: Data engineering projects, Data engineer project ideas, data project sources, data analytics project sources, data project portfolio _____________________________________________________________ Subscribe: https://www.youtube.com/channel/UCmLGJ3VYBcfRaWbP6JLJcpA?sub_confirmation=1 _____________________________________________________________ About me: I have spent my career focused on all forms of data. I have focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. I have also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. I privately consult on data science and engineering problems both solo as well as with a company called Acheron Analytics. I have experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data. *I do participate in affiliate programs, if a link has an "*" by it, then I may receive a small portion of the proceeds at no extra cost to you.

When I first started in the data world, no one around me used the term data pipeline. I heard terms like integrations, automations and ETL. In fact, I am not even sure when I first came across the term. But if you’re a data engineer in this modern era, then much of your time is spent, building, maintaining and keeping data pipelines running smooth. Even with AI, you’re probably still finding yourself opening up 3,000 line queries, and the occasional custom data pipeline system. If you enjoyed this video, check out some of my other top videos. Common Data Pipeline Patterns You’ll See in the Real World - Types Of Data Pipelines You'll Build https://youtu.be/htAipJ6yYFs What Is BigQuery - Breaking Down What BigQuery Is And Diving Into A Hands On Walkthrough https://youtu.be/pud-vuNE15g If you're looking for help ingesting your data in batch or real time, then you need to check out Estuary - https://bit.ly/4eQC3oQ If you'd like to read up on my updates about the data field, then you can sign up for our newsletter here. https://seattledataguy.substack.com/​​ Or check out my blog https://www.theseattledataguy.com/ And if you want to support the channel, then you can become a paid member of my newsletter https://seattledataguy.substack.com/subscribe Tags: Data engineering projects, Data engineer project ideas, data project sources, data analytics project sources, data project portfolio _____________________________________________________________ Subscribe: https://www.youtube.com/channel/UCmLGJ3VYBcfRaWbP6JLJcpA?sub_confirmation=1 _____________________________________________________________ About me: I have spent my career focused on all forms of data. I have focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. I have also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. I privately consult on data science and engineering problems both solo as well as with a company called Acheron Analytics. I have experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data. *I do participate in affiliate programs, if a link has an "*" by it, then I may receive a small portion of the proceeds at no extra cost to you.

Where do you start as a consultant. A few weeks back I put out a quick intro video talking about consulting. Now here is the first video. Feel free to let me know if you have quesitons. If you're considering becoming a freelancer, then you should check out this video. If you enjoyed this video, check out some of my other top videos. Common Data Pipeline Patterns You’ll See in the Real World - Types Of Data Pipelines You'll Build https://youtu.be/htAipJ6yYFs What Is BigQuery - Breaking Down What BigQuery Is And Diving Into A Hands On Walkthrough https://youtu.be/pud-vuNE15g If you'd like to read up on my updates about the data field, then you can sign up for our newsletter here. https://seattledataguy.substack.com/​​ Or check out my blog https://www.theseattledataguy.com/ And if you want to support the channel, then you can become a paid member of my newsletter https://seattledataguy.substack.com/subscribe Tags: Data engineering projects, Data engineer project ideas, data project sources, data analytics project sources, data project portfolio _____________________________________________________________ Subscribe: https://www.youtube.com/channel/UCmLGJ3VYBcfRaWbP6JLJcpA?sub_confirmation=1 _____________________________________________________________ About me: I have spent my career focused on all forms of data. I have focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. I have also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. I privately consult on data science and engineering problems both solo as well as with a company called Acheron Analytics. I have experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data. *I do participate in affiliate programs, if a link has an "*" by it, then I may receive a small portion of the proceeds at no extra cost to you.

I've been having dozens of conversations with data leaders asking them about whats changing in the data world. And of course, what is staying the same. This week I'll be talking to Heqing Huang. He's been leading data teams now for several years and I wanted to know what he is seeing. How is he using LLMs in his daily workflows? What still hasn't changed? Come join me this week and bring questions.

Over the past few years I've seen the data and software world turn on it's head in some ways and stay the same in others. It can't be denied that LLMs have impacted the way many people write code. Maybe you're a hard code trad-coder. Or 100% in to vibe coding. The truth is, we are still early. With that, I've been working with Dorian a lot over the past two months as we are working to build tooling to try to solve several of the problems we see many data and software teams face. This includes: - Determinism - Token Costs - The Jr. Problem To name a few! So I asked Dorian to come and join me on a chat where we'll discuss his view on these problems and where he thinks all of this is going. What questions should I ask? And if you're not already follow Codestrap! https://medium.com/codestrap/the-future-shape-of-developers-talent-development-and-engineering-organizations-303f1742e5fc

So, someone told you it's easy to become a consultant or freelancer? Well, its not, but that doesn't mean you shouldn't! Hey, if you haven't met me before my name is Ben Rogojan. I've been working as a full-time freelancer for almost half a decade now and been and off and on one for the full decade. I left my job at Facebook in 2021 and have been consulting ever since. I get asked from time to time where to get started. So over the next few weeks I'll be releasing this mini-bootcamp. There. will be about 4-5 videos. If you want to make sure you don't miss the videos, then sign up below. https://forms.gle/dw2Tt2VGttiET3rG7 If you'd like to read up on my updates about the data field, then you can sign up for our newsletter here. https://seattledataguy.substack.com/​​ Or check out my blog https://www.theseattledataguy.com/ And if you want to support the channel, then you can become a paid member of my newsletter https://seattledataguy.substack.com/subscribe Tags: Data engineering projects, Data engineer project ideas, data project sources, data analytics project sources, data project portfolio _____________________________________________________________ Subscribe: https://www.youtube.com/channel/UCmLGJ3VYBcfRaWbP6JLJcpA?sub_confirmation=1 _____________________________________________________________ About me: I have spent my career focused on all forms of data. I have focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. I have also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. I privately consult on data science and engineering problems both solo as well as with a company called Acheron Analytics. I have experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data. *I do participate in affiliate programs, if a link has an "*" by it, then I may receive a small portion of the proceeds at no extra cost to you.

At some point, if you work in data, whether you’re an analyst or a data engineer. You’re going to have to do it. You’re going to have to backfill a table. Actually, it’ll probably be pretty early in your career. Backfilling or rerunning a pipeline is just a necessity, AI or not. There are plenty of reasons why you might need to backfill a table..sadly. Talking to data engineers…many of them dislike the process of backfilling. So let’s start there. Let’s discuss why we backfill as data teams and why we dislike it so much. Also thanks - Marko Pavliuk for the thumbnail design If you enjoyed this video, check out some of my other top videos. Common Data Pipeline Patterns You’ll See in the Real World - Types Of Data Pipelines You'll Build https://youtu.be/htAipJ6yYFs What Is BigQuery - Breaking Down What BigQuery Is And Diving Into A Hands On Walkthrough https://youtu.be/pud-vuNE15g If you're looking for help ingesting your data in batch or real time, then you need to check out Estuary - https://bit.ly/4eQC3oQ If you'd like to read up on my updates about the data field, then you can sign up for our newsletter here. https://seattledataguy.substack.com/​​ Or check out my blog https://www.theseattledataguy.com/ And if you want to support the channel, then you can become a paid member of my newsletter https://seattledataguy.substack.com/subscribe Tags: Data engineering projects, Data engineer project ideas, data project sources, data analytics project sources, data project portfolio _____________________________________________________________ Subscribe: https://www.youtube.com/channel/UCmLGJ3VYBcfRaWbP6JLJcpA?sub_confirmation=1 _____________________________________________________________ About me: I have spent my career focused on all forms of data. I have focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. I have also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. I privately consult on data science and engineering problems both solo as well as with a company called Acheron Analytics. I have experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data. *I do participate in affiliate programs, if a link has an "*" by it, then I may receive a small portion of the proceeds at no extra cost to you.

If you enjoyed this video, check out some of my other top videos. When I first started in the data world, it was common that many data teams would build their own data pipeline solutions. There were still dozens of options in terms of off the shelf tools of course, nevertheless, you’d see custom pipelines developed everywhere. In 2026, I am seeing less of this. In fact, in many cases data teams would go straight to picking tools or solutions. But let’s say you do want to go down this route. You want to build your own data pipeline solution? How would you do it? That's what we will focus on in this video! If you're looking to learn more about data engineering, then check out the videos below. What Has Changed In Data Engineering In The Past Decade - From The Cloud To AI https://youtu.be/YLt2E_2qlpQ Common Data Pipeline Patterns You’ll See in the Real World - Types Of Data Pipelines You'll Build https://youtu.be/htAipJ6yYFs If you're looking for help ingesting your data in batch or real time, then you need to check out Estuary - https://bit.ly/4eQC3oQ If you'd like to read up on my updates about the data field, then you can sign up for our newsletter here. https://seattledataguy.substack.com/​​ Or check out my blog https://www.theseattledataguy.com/ And if you want to support the channel, then you can become a paid member of my newsletter https://seattledataguy.substack.com/subscribe Tags: Data engineering projects, Data engineer project ideas, data project sources, data analytics project sources, data project portfolio _____________________________________________________________ Subscribe: https://www.youtube.com/channel/UCmLGJ3VYBcfRaWbP6JLJcpA?sub_confirmation=1 _____________________________________________________________ About me: I have spent my career focused on all forms of data. I have focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. I have also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. I privately consult on data science and engineering problems both solo as well as with a company called Acheron Analytics. I have experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data. *I do participate in affiliate programs, if a link has an "*" by it, then I may receive a small portion of the proceeds at no extra cost to you.

The data industry is at an interesting point right now. AI is accelerating development..It feels like every week theres a new start up or model that you'd better be paying attention to. Data stacks keep getting more complex, just add one more layer. And yet many teams still struggle with the same core challenge: Turning data into actual business outcomes. This week I’m sitting down with Tim Frazer, Director of Data Engineering , to talk about what it really takes to build data platforms and teams that drive growth. Tim has worked across consulting, architecture, and leadership roles helping companies streamline data workflows, reduce warehouse costs, and actually extract value from their data. I hope to see you there. Let me know if you have any questions for Tim!

Many organizations invest heavily in data but still struggle to turn insights into action. Kacie McCarthy, who I'll be talking to has worked as a data and operations leader at large enterprises. That's why in this discussion, we explore how data teams can work more closely with operations, make better decisions under uncertainty, and become true strategic partners to the business not just task takers. If you have any questions post them below. I am really excited for this chat!

Whether you’re working at a large enterprise or a small business, there has likely been some need to take data out of the various source systems, process it, and then use it for either operational or analytical purposes. Add in a few lines of code or a low-code solution, and the term data pipeline might start getting thrown around. This might make some data engineers angry, but if you think about it, someone extracting data from a data source into Excel, adding in VLOOKUPs, some data cleansing via formulas and IFELSE() statements is essentially building a data pipeline…. Ok, it’s not the exact same thing, but when you stop and think about it, it can functionally solve a similar problem(although often in a more limited and specific way) My point is that there are a lot of different ways and reasons people build data pipelines. So, to kick off 2026, I wanted to discuss some of the key reasons data pipelines exist and the types of pipelines you will run into. A few months back, I wrote an article about what hasn’t changed in the data world. And much of what hasn’t changed are the problems we face. Of course, there are also plenty of things that have changed in the data world since I started. For example, the technologies and practices we use. Even the words and terms we use, although mostly the same, have changed. Whether you like it or not. When I started, no one used the term analytics engineer, and even the concept of a data engineer was still relatively new(at least in terms of how popular it later became). In the same way, I have seen plenty of things change. Some of these changes are temporary, I believe (like my first point), others are likely larger trends. So let’s dive into what’s changed in the data world in the last decade. If you enjoyed this video, check out some of my other top videos. Intro To Databricks - What Is Databricks https://www.youtube.com/watch?v=QNdiGZFaUFs The Inconvenient Truths of Self-Service Analytics - Tell Me Lies Tell Me Sweet Little Lies https://www.youtube.com/watch?v=vIclW1EqeUg If you're looking for help ingesting your data in batch or real time, then you need to check out Estuary - https://bit.ly/4eQC3oQ If you'd like to read up on my updates about the data field, then you can sign up for our newsletter here. https://seattledataguy.substack.com/​​ Or check out my blog https://www.theseattledataguy.com/ And if you want to support the channel, then you can become a paid member of my newsletter https://seattledataguy.substack.com/subscribe Tags: Data engineering projects, Data engineer project ideas, data project sources, data analytics project sources, data project portfolio _____________________________________________________________ Subscribe: https://www.youtube.com/channel/UCmLGJ3VYBcfRaWbP6JLJcpA?sub_confirmation=1 _____________________________________________________________ About me: I have spent my career focused on all forms of data. I have focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. I have also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. I privately consult on data science and engineering problems both solo as well as with a company called Acheron Analytics. I have experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data. *I do participate in affiliate programs, if a link has an "*" by it, then I may receive a small portion of the proceeds at no extra cost to you.

A few months back, I wrote an article about what hasn’t changed in the data world. And much of what hasn’t changed are the problems we face. Of course, there are also plenty of things that have changed in the data world since I started. For example, the technologies and practices we use. Even the words and terms we use, although mostly the same, have changed. Whether you like it or not. When I started, no one used the term analytics engineer, and even the concept of a data engineer was still relatively new(at least in terms of how popular it later became). In the same way, I have seen plenty of things change. Some of these changes are temporary, I believe (like my first point), others are likely larger trends. So let’s dive into what’s changed in the data world in the last decade. If you enjoyed this video, check out some of my other top videos. Intro To Databricks - What Is Databricks https://www.youtube.com/watch?v=QNdiGZFaUFs The Inconvenient Truths of Self-Service Analytics - Tell Me Lies Tell Me Sweet Little Lies https://www.youtube.com/watch?v=vIclW1EqeUg If you're looking for help ingesting your data in batch or real time, then you need to check out Estuary - https://bit.ly/4eQC3oQ If you'd like to read up on my updates about the data field, then you can sign up for our newsletter here. https://seattledataguy.substack.com/​​ Or check out my blog https://www.theseattledataguy.com/ And if you want to support the channel, then you can become a paid member of my newsletter https://seattledataguy.substack.com/subscribe Tags: Data engineering projects, Data engineer project ideas, data project sources, data analytics project sources, data project portfolio _____________________________________________________________ Subscribe: https://www.youtube.com/channel/UCmLGJ3VYBcfRaWbP6JLJcpA?sub_confirmation=1 _____________________________________________________________ About me: I have spent my career focused on all forms of data. I have focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. I have also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. I privately consult on data science and engineering problems both solo as well as with a company called Acheron Analytics. I have experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data. *I do participate in affiliate programs, if a link has an "*" by it, then I may receive a small portion of the proceeds at no extra cost to you.

Self-service analytics is a hoax. A myth. The so-called holy grail. If you’ve worked in data long enough, you’ve heard that line. Maybe you’ve even thought it yourself, like the time your CEO asked for a “quick data pull” despite you building them a perfectly good dashboard that already answered the question. When I first entered the data world, self-service analytics was everywhere. Tableau was pushing it hard, then Salesforce bought them for $15.7 billion, and every vendor promised it would free analysts from endless requests. A decade later, we’re still chasing that dragon. Now the narrative has shifted: dashboards are “dead,” or the problem was that Tableau and Looker just “never did it right.” Say that self-service is a failure in the wrong room, and someone will tell you, “You were just doing it wrong.” Wherever you stand on the debate, I want to explore where self-service has struggled, and what might actually work next.... If you enjoyed this video, check out some of my other top videos. Intro To Databricks - What Is Databricks https://youtu.be/QNdiGZFaUFs What Is Snowflake - Breaking Down What Snowflake Is, How Snowflake Credits Work And More https://youtu.be/GuM6dQGRFyQ If you'd like to read up on my updates about the data field, then you can sign up for our newsletter here. https://seattledataguy.substack.com/​​ Or check out my blog https://www.theseattledataguy.com/ And if you want to support the channel, then you can become a paid member of my newsletter https://seattledataguy.substack.com/subscribe Tags: Data engineering projects, Data engineer project ideas, data project sources, data analytics project sources, data project portfolio _____________________________________________________________ Subscribe: https://www.youtube.com/channel/UCmLGJ3VYBcfRaWbP6JLJcpA?sub_confirmation=1 _____________________________________________________________ About me: I have spent my career focused on all forms of data. I have focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. I have also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. I privately consult on data science and engineering problems both solo as well as with a company called Acheron Analytics. I have experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data. *I do participate in affiliate programs, if a link has an "*" by it, then I may receive a small portion of the proceeds at no extra cost to you.

When I started in the data world back in 2015 Hadoop was at it’s peak. Actually I happened to be scrolling through an old instagram account and found a picture from a DAMA conference where Horton Works sponsored it(you can see it below). At the time, Hadoop and its ecosystem were everywhere, Hortonworks, Cloudera, MapR, each promising to reshape the future of data. Fast forward just ten years and many newer practitioners don’t even recognize those names. And yet, in data engineering, a decade is barely enough time for fundamentals to change. Underneath the hype cycles and new logos, many of the same challenges remain. So let's talk about it! If you enjoyed this video, check out some of my other top videos. What Is BigQuery - Breaking Down What BigQuery Is And Diving Into A Hands On Walkthrough https://youtu.be/pud-vuNE15g What Is Snowflake - Breaking Down What Snowflake Is, How Snowflake Credits Work And More https://youtu.be/GuM6dQGRFyQ If you'd like to read up on my updates about the data field, then you can sign up for our newsletter here. https://seattledataguy.substack.com/​​ Or check out my blog https://www.theseattledataguy.com/ Also, if your team is trying to figure out whether you should build batch or real-time pipelines, then you should check out Estuary so you no longer have to choose(I am an advisor for Estuary)! https://estuary.dev/ And if you want to support the channel, then you can become a paid member of my newsletter https://seattledataguy.substack.com/subscribe Tags: Data engineering projects, Data engineer project ideas, data project sources, data analytics project sources, data project portfolio _____________________________________________________________ Subscribe: https://www.youtube.com/channel/UCmLGJ3VYBcfRaWbP6JLJcpA?sub_confirmation=1 _____________________________________________________________ About me: I have spent my career focused on all forms of data. I have focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. I have also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. I privately consult on data science and engineering problems both solo as well as with a company called Acheron Analytics. I have experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data. *I do participate in affiliate programs, if a link has an "*" by it, then I may receive a small portion of the proceeds at no extra cost to you.

If you enjoyed this video, check out some of my other top videos. Top Courses To Become A Data Engineer In 2022 https://www.youtube.com/watch?v=kW8_l57w74g What Is The Modern Data Stack - Intro To Data Infrastructure Part 1 https://www.youtube.com/watch?v=-ClWgwC0Sbw If you would like to learn more about data engineering, then check out Googles GCP certificate https://bit.ly/3NQVn7V If you'd like to read up on my updates about the data field, then you can sign up for our newsletter here. https://seattledataguy.substack.com/​​ Or check out my blog https://www.theseattledataguy.com/ And if you want to support the channel, then you can become a paid member of my newsletter https://seattledataguy.substack.com/subscribe Tags: Data engineering projects, Data engineer project ideas, data project sources, data analytics project sources, data project portfolio _____________________________________________________________ Subscribe: https://www.youtube.com/channel/UCmLGJ3VYBcfRaWbP6JLJcpA?sub_confirmation=1 _____________________________________________________________ About me: I have spent my career focused on all forms of data. I have focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. I have also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. I privately consult on data science and engineering problems both solo as well as with a company called Acheron Analytics. I have experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data. *I do participate in affiliate programs, if a link has an "*" by it, then I may receive a small portion of the proceeds at no extra cost to you.