Seattle Data Guy

Going from data engineer or analyst to manager isn't easy. There often is no training or preparation; just here you go, lead this team. If they are lucky, maybe someone in leadership recommends a book for them to read. That's it. Suddenly, you have to deal with project prioritization, contractors, employees, vendors, budgets, and so much more. That's why, over the past few months, I have been talking to data directors and managers to understand what they wish they would have known when they started their journey. So here are 7 articles and videos you can use to quickly get up to speed about the challenges of leading data teams. 1. Thinking Like an Owner: Elevating Your Data Team's Impact In 2025 https://lnkd.in/gYAi_Eky 2. The Data Leader’s Guide to Crucial Conversations https://lnkd.in/gvmtHsuY 3. Data Teams: It’s Time to Touch Grass https://lnkd.in/endXGigJ 4. Don’t Lead a Data Team Before Reading This https://lnkd.in/gqGDUTZA 5. How To Manage Data Teams Successfully - Asking A Director Of Data Architecture And Governance with Jeff Nemecek https://lnkd.in/g6384SCB 6. Building Credibility As A Data Leader https://lnkd.in/e9tMvPwc 7. Becoming A Better Data Engineer - Tips On Translating Business Requirements https://lnkd.in/gnQNyZbs What articles, books or videos would you recommend people read?

A lot of data teams want a seat at the table without doing the work to earn it. They want to be seen as strategic partners. They want influence. But they don’t always understand what actually drives the business. If you can’t explain how your company makes money, what levers move the needle, or even give a basic overview of its history and context, why should leadership see you as anything more than a task taking organization? We’ve spent years talking about how business leaders need to become more data-fluent. But not enough people are saying the quiet part out loud: data professionals need to become business-fluent. How? By putting in the effort. That means talking to stakeholders, learning how your industry works, understanding your company’s position in the market, and figuring out how data can push it forward. Someone has to bridge the gap between technical execution and business outcomes, and if you lead a data team, that someone is probably you. The best data engineers, analysts, and scientists I’ve worked with didn’t wait around for executive approval. They didn’t need permission to drive impact, they found the problems, solved them, and made sure the right people noticed. It’s not easy. And it’s especially hard to connect the technical work we do with the business outcomes leadership actually cares about. https://lnkd.in/gMR7Jw5J

Dear CEOs and data executives, Your data team doesn’t need more headcount or another data platform to find value from data. No matter what your vendor or consulting company is trying to convince you of... They need to be able to focus! Right now, they’re drowning in: - Random asks and flights of fancy - Tool sprawl(Of course you should be using Snowflake, Bigquery And Databricks right?) - Broken data pipelines and schema changes You don’t need another dashboard for the business to ignore. You don’t need more people that go through the motions of building data pipelines and automated workflows. You need clarity(and honestly the willingness to say no). Clarity on: - What matters to the business - What matter to the industry as a whole - What success looks like Until you fix the upstream bias for reaction and align on priorities, you’re just scaling dysfunction. Value doesn’t come from doing more. If this sounds familiar and your team needs help, feel free to reach out.

If you’re a data engineer or data analyst, then you’ve likely at least heard of Airflow. Apache Airflow is one of the most popular open-source workflow orchestration solutions that gets used for data pipelines. A few years back I started to notice Airflow was starting to get some heat. So I wrote an article titled “Should You Use Airflow”. Despite the criticism, Airflow continues to play a pivotal role, much like PHP of the data sector – often criticized but extensively relied upon. And I see it everywhere. That doesn’t mean there aren’t other solutions out there! This past week it was awesome to see Estuary and Orchestra partner together to make data workflows easier without spinning up an Airflow instance. Congrats to both David Yaffe and Hugo Lu! Y’all can read more about it here - https://lnkd.in/gvXDc2MS

Let's talk data! If you want to learn how data teams are actually using data, AI and machine learning, then one of the best ways is to talk to other professionals and hear about the real challenges and problems they are solving and how they solved it. Sure, you can build another toy ML model or test out a new tool, but until you're working in production with real problems, like messy data and scale, it'll never really connect. But a great way to learn is by watching what others do. So, I wanted to highlight a few that are coming up. Here are just 7 events you should check out or may have missed. 1. Right-Time Data Integration for MotherDuck with Zulfikar Qureshi https://lnkd.in/g_wWiMJJ 2. AI Hackathon In Stockholm With Oskar Eriksson and Johannes Sunden https://lnkd.in/g2rsQask And here are some events and podcasts you may have missed! 3. The Reality of Data Engineering What Data Teams Get Wrong with Tim Frazer 🚀 https://lnkd.in/gcjDrWeC 4. Operationalizing Data - Moving Beyond Analytics, Turning Data Into Action with Kacie McCarthy, MCR https://lnkd.in/gb4Mc_sN 5. CxC Ep20: Winning the AI Software Race With Dorian Smiley, Connor Deeks and Chad Wahlquist https://lnkd.in/gN_2DTrb 6. Frameworks data leader and ICs should know to maximize their impact with Tessa Xie https://lnkd.in/gKX5Aeqd 7. 10 Years Of Building Data Pipelines - What Has Changed And What Has Stayed The Same with Daniel Palma https://lnkd.in/g7_VZ4Sj

If you’re a data engineer or data analyst, then you’ve likely at least heard of Airflow. Apache Airflow is one of the most popular open-source workflow orchestration solutions that gets used for data pipelines. A few years back I started to notice Airflow was starting to get some heat. So I wrote an article titled “Should You Use Airflow”. Despite the criticism, Airflow continues to play a pivotal role, much like PHP of the data sector – often criticized but extensively relied upon. And I see it everywhere. That doesn’t mean there aren’t other solutions out there! This past week it was awesome to see Estuary and Orchestra partner together to make data workflows easier without spinning up an Airflow instance. Congrats to both David Yaffe and Hugo Lu! Y’all can read more about it here - https://lnkd.in/gvXDc2MS

It’s finally time...companies can get rid of data engineers and save millions. Just check out what the data engineering subreddit has to say! - No one understands this 180 line query someone vibe coded - Vibe / Citizen Developers bringing our Datawarehouse to it's knee's - We're paying $100,000 for Databricks to manage 100,000 rows

It’s finally time...companies can get rid of data engineers and save millions. Just check out what the data engineering subreddit has to say! - No one understands this 180 line query someone vibe coded - Vibe / Citizen Developers bringing our Datawarehouse to it's knee's - We're paying $100,000 for Databricks to manage 100,000 rows

I don't know who needs to hear this, but not every dashboard needs to be real-time. That being said, it is becoming far easier to implement real-time data workflows! A few years ago, real-time meant: - Kafka clusters you had to babysit - Custom consumers and producers - A dedicated data engineering team just to keep it alive But now you can pull tools off the shelf that help remove this barrier to entry and in cases of tools like Estuary make it easy to switch between batch and real-time. The barrier is lower than ever. The real question is...does your use case actually need it? If you're trying to answer that question or even how to get started you should check out the free webinar Zulfikar Qureshi is hosting showing you how to load data into MotherDuck at the right time! You can sign up here - https://lnkd.in/g-XUCRF6

It’s 2030, and your boss just asked you to pull data to help better segment your users and understand their behaviors. You open your laptop, and three agents spin up immediately You provide the general ask, and they start parsing your information schema, past query history, previous analysis, as well as current trends and market behaviors from public databases. Python scripts are being written. Queries run. Do you know how any of it works? No. Do you know the difference between a right and a left join? No. But those are meaningless details. Thousands of lines of code, and who knows how many SnowBrick credits later, you’ve built a customer segment table. Do you run any form of QA check? Nah, it’s probably right. Plus, you’re busy scrolling through your AI-generated TikToks and probably couldn’t focus long enough to check. You will know nothing and be happy - Full article here - https://lnkd.in/gV4MX_-5