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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
836 implied HN points 14 Mar 24
  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.
482 implied HN points 22 Feb 24
  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.
694 implied HN points 14 Feb 24
  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.
Get a weekly roundup of the best Substack posts, by hacker news affinity:
1236 implied HN points 09 Jan 24
  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.
1165 implied HN points 02 Jan 24
  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.
871 implied HN points 26 Dec 23
  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.
541 implied HN points 07 Nov 23
  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.
930 implied HN points 12 Aug 23
  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.
1048 implied HN points 11 Apr 23
  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.
671 implied HN points 23 Apr 23
  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.