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SeattleDataGuy's Newsletter focuses on the intricacies of data engineering, MLOps, and data science, offering insights on career development, technical comparisons, and best practices in data management. It provides practical advice on overcoming challenges, maximising impact, and understanding the evolving landscape of data-driven fields.

Career Development Data Engineering Machine Learning Operations (MLOps) Data Science Data Management Techniques Team Organization Strategies Learning and Development in Data Fields

The hottest Substack posts of SeattleDataGuy’s Newsletter

And their main takeaways

From Average To Exceptional: 5 Skills To Help You Become The Data Expert Everyone Wants To Work With

494 implied HN points • 19 Feb 25
💼 Business Data science Career development Communication Analytics Team Collaboration
  1. Always focus on the real problem behind a request, not just what is being asked. This helps you deliver better solutions that actually meet the business needs.
  2. Using clear frameworks can help organize your thoughts and make complex investigations easier. A structured approach leads to clearer communication and better results.
  3. Keep your communication simple and focused on what matters to your stakeholders. This helps everyone stay on the same page and reduces confusion.

Why Your Data Infrastructure Migration Project Will Fail (And How to Succeed)

376 implied HN points • 12 Feb 25
🕹 Technology Data Engineering Infrastructure Software Development Project management Change Management
  1. Having a clear plan is crucial for successful data migration projects. You need to know what to move and in what order to avoid chaos.
  2. Ownership of the migration process is important. There should be a clear leader or team responsible to keep everything on track.
  3. Testing data after migration is a must. Just moving the data doesn't guarantee that it works the same way, so check for any discrepancies.

Is It Time to Say Goodbye to Data Engineers?

812 implied HN points • 06 Feb 25
🕹 Technology Data Engineering Software Development Data Management Business Intelligence Analytics
  1. Data engineers are often seen as roadblocks, but cutting them out can lead to major problems later on. Without them, the data can become messy and unmanageable.
  2. Initially, removing data engineers may seem like a win because things move quickly. However, this speed can cause chaos as data quality suffers and standards break down.
  3. A solid data strategy needs structure and governance. Rushing without proper planning can lead to a situation where everything collapses under the weight of disorganization.

Breaking Into Data: How to Land A Job in a Competitive Market

459 implied HN points • 28 Jan 25
💼 Business Job search Career development Networking Interview Prep Skills Training
  1. Focus on your skills rather than specific job titles. Job titles change all the time, so search by what you can do instead.
  2. Prepare well for interviews ahead of time. Make a study plan and find out the topics to focus on, so you don’t get stressed last minute.
  3. Build real connections with people in your field. Attend events, follow up with new contacts, and engage on platforms like LinkedIn to create opportunities.

Data Leadership Done Right: Focusing on Work That Matters

506 implied HN points • 14 Jan 25
💼 Business Career development Impact assessment Project management Team Dynamics
  1. Focus on what really matters in your work to create an impact, rather than just completing tasks. It's important to understand the value of what you do.
  2. As you grow in your role, ask yourself what projects move the needle for your organization. This questioning mindset helps drive meaningful change.
  3. To advance in your career, be proactive in identifying valuable projects instead of waiting for your manager to tell you what to do. Taking initiative is key to success.
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From Boom to Bundle: The Great Consolidation of Data Tools

400 implied HN points • 17 Jan 25
🕹 Technology Data Tools Mergers & Acquisitions Analytics Data science Business Intelligence
  1. The data tools market is seeing a lot of consolidation lately, with companies merging or getting acquired. This means there are fewer companies competing, but it can lead to better tools overall.
  2. Acquisitions can be a mixed bag for customers. While some products improve after being bought, others might lose their features or support, making it risky for users.
  3. There's a push for bundled data solutions where customers want fewer, but more comprehensive tools. This could change how data companies operate and how startups survive in the future.

5 Key Predictions for the Data Industry in 2025

612 implied HN points • 07 Jan 25
🕹 Technology Data Analysis AI Data Tools Business Intelligence Data Governance
  1. Iceberg will become popular, but not every business will adopt it. Many companies want simpler solutions that fit their needs without needing lots of complicated tools.
  2. SQL isn't going anywhere; it still works well for managing and querying data. People have realized that a bit of order in data is important for getting meaningful insights.
  3. AI use will become more practical, focusing on real-world applications rather than just hype. Companies will find specific tasks to automate using AI, making their workflows more efficient.

5 Habits Of Highly Effective Data Engineers To Master in 2025

800 implied HN points • 20 Dec 24
🕹 Technology Data Engineering Career growth Technical Skills Project management Professional development
  1. Being proactive means solving problems before they become bigger issues. If you see something that can be improved, go ahead and make that change instead of waiting for someone else to do it.
  2. Make sure your contributions are visible, so people recognize your work. Share your successes and updates with your team and leadership to build a stronger reputation.
  3. Become the go-to person for a specific area in your company. Focus on something valuable that can help others succeed, and make sure to share your knowledge and support with your team.

Beyond Big Tech: The Reality Of Data Engineering Outside Silicon Valley

847 implied HN points • 14 Dec 24
🕹 Technology Data Engineering Big Tech Infrastructure Data Systems Business Processes
  1. Working in big tech offers many advantages like better tools and a strong focus on data. This environment makes it easier to get work done quickly and efficiently.
  2. Many companies outside big tech struggle with data because it's not their main focus. They often use a mix of different tools that don't work well together, leading to confusion.
  3. Without a strong data leader, companies may find it hard to prioritize data spending. If data isn't tied to profits, it's tougher to justify investing time and money into it.

Thinking Like an Owner: Elevating Your Data Team's Impact In 2025

788 implied HN points • 30 Nov 24
💼 Business Data Analysis Team Management Career growth Performance Metrics
  1. Data teams should focus on projects that really matter to the business, not just completing tasks. It's important to pick work that makes a difference.
  2. Understanding how your business works is key to finding valuable projects. Ask questions about the data to see what's impacting your important metrics.
  3. Shift your mindset from being a regular team member to thinking like a business owner. This means taking initiative and seeking out projects that align with overall business goals.

Holy Grails of Data: Self-Service, Single Truths, and the Role of AI

365 implied HN points • 27 Dec 24
🕹 Technology Data science AI Analytics Business Intelligence Machine Learning
  1. Self-service analytics is still a goal for many companies, but it often falls short. Users might struggle with the tools or want different formats for the data, leading to more questions instead of fewer.
  2. Becoming truly data-driven is a challenge for many organizations. Trust issues with data, preference for gut feelings, and poor communication often get in the way of making informed decisions.
  3. People need to be data literate for businesses to succeed with data. The data team must present insights clearly, while business teams should understand and trust the data they work with.

From 9-to-5 to Building a Career Portfolio: How One Consulting Project Changed My Life

612 implied HN points • 08 Dec 24
💼 Business Career development Consulting Networking Personal Branding Skill Building
  1. Building a solid skill set is crucial early in your career. Try out different skills and projects to find what you enjoy and what works for you.
  2. Seizing opportunities is key. Say yes to things that can help you grow, and be proactive about creating your own chances.
  3. Think of your career like building an investment portfolio. Every skill, project, and connection adds value, so keep investing in yourself and your network.

Thank You for 100k: A Journey Through 2024 and Beyond

294 implied HN points • 31 Dec 24
💼 Business Consulting Data Analysis Content creation Leadership Community Building
  1. In 2024, I gained over 100,000 subscribers on both YouTube and Substack. I really appreciate the support and plan to create even better content next year.
  2. This year showed trends like cloud data migrations and smaller, fractional data teams, which are changing how companies handle data. It's important to keep an eye on these shifts in the data world.
  3. Looking ahead to 2025, I want to finish my book on data leadership and offer more webinars and mini-courses. I'm excited to engage even more with my readers and build a community.

7 Lessons I Learned the Hard Way From 9+ Years as a Data Engineer

730 implied HN points • 21 Nov 24
🕹 Technology Data Engineering Software Development Career Advice Project management Skills Training
  1. It's important to avoid building complex systems just for the sake of it. Focus on creating infrastructure that actually helps your team and the business.
  2. If you don’t plan your data model, you’ll end up with a messy one. Always take the time to design it properly to make future work easier.
  3. Good communication is really powerful. Being able to share your ideas clearly can help you get support and make a bigger impact in your projects.

Kickstart Your Data Engineering Career: 9 Resources and Templates You Need

541 implied HN points • 14 Nov 24
🚌 Education Data Engineering
  1. Use the 100-Day Data Engineering Crash Course to start learning the basics of data engineering. It covers important topics like SQL, programming, and Cloud technologies.
  2. Creating your own data projects can help you stand out. The Data Engineering Side Project Idea Template will guide you in planning unique projects that add value.
  3. Prepare well before job interviews with the Data Engineer Interview Study Guide. Always check with the recruiter about what to study so you can be ready.

Why Data Teams Need to Understand Metrics: A Look at Starbucks’ Comparable Store Sales

447 implied HN points • 08 Nov 24
💼 Business Metrics Data Analysis Corporate strategy Revenue management
  1. Data teams need to know the main numbers that matter for their business. This helps them understand how the company is performing.
  2. High-level metrics like revenue and expenses can seem too big to grasp. Breaking these down into smaller parts makes them easier to understand.
  3. These smaller, detailed metrics can reveal valuable insights that affect decisions and strategies for the business.

Data Sharing in the Real World: Why SFTP Remains Essential for Companies

400 implied HN points • 31 Oct 24
🕹 Technology Data Sharing Data Engineering Standardization
  1. SFTP stands for Secure File Transfer Protocol, and it's a popular method for companies to send and receive data securely, like sending packages in the digital world. Many businesses, even big tech ones, still rely on SFTP instead of newer methods.
  2. Setting up SFTP jobs requires careful planning, especially for user authentication and file encryption. Using SSH keys and methods like PGP encryption helps ensure the data remains safe during transfers.
  3. Although there are more advanced data-sharing technologies emerging, SFTP isn't going away anytime soon. Many companies still rely on SFTP for their data needs, showing its continued importance in the industry.

Should You Build a Custom Data Orchestration System? Here’s What to Consider

317 implied HN points • 23 Oct 24
🕹 Technology Data Engineering Software Development Automation System Architecture
  1. Building your own data orchestration system can lead to many challenges, like handling dependencies and scheduling tasks correctly. It's important to think if it's really necessary or if existing tools will work better.
  2. A custom orchestrator needs to manage various functions like logging, alerting, and integrating with other tools. Without proper features, it can become complex and hard to maintain.
  3. Before you decide to create your own solution, consider what makes it different and better than what's already available. Make sure to also think about how you’ll get people to use your new system.

Ten Of The Funniest Data Memes

1236 implied HN points • 09 Jan 24
🎭️ Culture Humor
  1. Working in data often involves struggles that can be humorously captured in memes.
  2. Creating dashboards that go unused is a common issue in many enterprises.
  3. Data scientists sometimes have to work with unreliable data, leading to additional challenges.

The Data Team Playbook: 50+ Resources For High-Performing Data Teams

836 implied HN points • 14 Mar 24
🕹 Technology Data Infrastructure Talent Acquisition Communication Influence
  1. Starting a career as a data team manager involves challenges and new skills, with resources like books to aid in the transition.
  2. Assisting team members in their career growth involves sharing helpful articles, guides, and videos.
  3. Improving project management, team culture, and communication are key elements in running successful data teams.

Growing From Analyst To Data Engineer In 100 Days

1165 implied HN points • 02 Jan 24
🕹 Technology Data Engineering Skills Development Community Building
  1. Breaking into data engineering may be easier through lateral moves, like from data analyst to data engineer.
  2. The 100-day plan discussed is not meant to master data engineering but to help commit to learning and identify areas for improvement.
  3. The plan includes reviewing basics, diving deeper, building a mini project, surveying tools, best practices, and committing to a final project.

Seattle Data Guy In 2024

871 implied HN points • 26 Dec 23
🕹 Technology Data Analysis Consulting Content creation Community Building
  1. Seattle Data Guy's work in 2023 involved filming videos, virtual conferences, and writing articles and newsletters.
  2. Client trends in 2023 showed shifts towards greenfield projects, solution design, marketing, and education.
  3. Popular articles in 2023 covered topics like data modeling, breaking out of tutorial hell, and essential templates for data analytics.

How to grow from a mid-level to senior Data Engineer

694 implied HN points • 14 Feb 24
🕹 Technology Data Engineering Software Engineering Learning Teamwork Leadership
  1. To grow from mid to senior level, it's important to continuously learn and improve, share new knowledge, work on code improvements, and become an expert in a certain domain.
  2. Making the team better is crucial - focus on mentoring, sharing knowledge, and creating a positive team environment. Think beyond individual tasks to impact the overall team outcomes.
  3. Seniority includes building not just technical solutions, but solutions that customers love. Challenge requirements, understand the business and product, and take initiative in problem-solving.

How I Left Facebook to Forge My Path in Data Consulting

482 implied HN points • 22 Feb 24
💼 Business Consulting Marketing Networking Resources Entrepreneurship
  1. Define your niche: Before starting a consulting business, determine what specific problems you aim to solve for clients.
  2. Attracting clients: Methods to find clients include content marketing, networking, referrals, sales outreach, and vendor partnerships.
  3. Creating a marketing funnel: Use frameworks like AIDA (Awareness, Interest, Desire, Action) to organize and target your content towards potential clients.

How To Fast Track Your Career As An Engineer

930 implied HN points • 12 Aug 23
💼 Business Career growth Software Engineering Tech industry Data Analysis Career Advice
  1. Focusing on impact in your work can accelerate your career growth and lead to more satisfying outcomes.
  2. To have more impact in tech, run towards unsolved problems, be scrappy in finding solutions, and prioritize ruthlessly.
  3. Impact can be achieved by reducing costs or increasing revenue, and understanding how your work contributes to these areas is essential for career advancement in engineering.

7 Essential Templates for Data Analytics Consulting

541 implied HN points • 07 Nov 23
💼 Business Consulting Templates Projects
  1. Have essential templates ready for data analytics consulting to speed up document creation and ensure clear processes.
  2. Key templates include a discovery template, proposal and SOW, basic deck, project kick-off email, onboarding checklist, and update email.
  3. Using templates like these can help in landing clients, maintaining communication, and streamlining project workflows.

Data Engineering Vs Machine Learning Pipelines

1048 implied HN points • 11 Apr 23
🕹 Technology Data Engineering Machine Learning
  1. Data engineering and machine learning pipelines are essential components for every company, but are often confused because they have different objectives.
  2. Data engineering pipelines involve data collection, cleaning, integration, and storage, while machine learning pipelines focus on data cleaning, feature engineering, model training, evaluation, registry, deployment, and monitoring.
  3. Both data and ML pipelines require careful consideration of computational needs to handle sudden changes, and understanding the differences between them is important for effective data processing and decision-making.

Becoming a Data Engineering Force Multiplier

671 implied HN points • 23 Apr 23
🕹 Technology Data Engineering Skills Project management Communication Resources
  1. Data engineering is crucial in today's data-driven landscape, with a growing demand for skilled professionals.
  2. Developing technical skills like architecture, data modeling, coding, testing, and CI/CD is essential for becoming a successful data engineer.
  3. Non-technical skills such as teaching, long-term project planning, and communication are equally important for data engineers to excel and become force multipliers.