Transitions
Career-change paths that translate prior work into data and AI role evidence.
15 pages
Data Analyst to Analytics Engineer
A practical transition path from analyst work to analytics engineering, covering SQL modeling, dbt workflows, metric ownership, tests, and portfolio proof.
Data Analyst to Data Engineer
Convert analyst work into data engineering evidence: source ownership, reusable SQL, pipeline automation, quality checks, and an interview story.
Data Engineer to Data Science
How data engineers can turn pipeline, data quality, and deployment work into modeling, evaluation, and product-decision evidence.
Data Scientist to Data Engineer
How data scientists can move into data engineering: role shift, transferable skills, engineering gaps, portfolio projects, and interviews.
Data Scientist to ML Engineer
How data scientists move into ML engineering with reviewable code, shipped artifacts, production-minded projects, and stronger interview stories.
DevOps to Data Engineering
How DevOps, SRE, and platform engineers can turn automation, DataOps, cloud work, and portfolio projects into data engineering evidence.
Game AI to LLM Agents
How Micheal Lanham connects game AI, reinforcement learning, multi-agent workflows, support assistants, and modern LLM agents.
Marketer to Analytics Engineer
How marketers can move into analytics engineering with SQL, BI, dbt, product analytics, dashboards, and metric ownership.
Nontraditional AI Engineering
How career breaks, medicine, freelancing, semiconductors, and startups can become credible AI engineering proof.
PM to Data Science
How project managers can move into data science through stakeholder work, KPIs, analytics projects, Python practice, and portfolio evidence.
Product Designer to Data PM
How product designers can move into data product management through discovery, SQL, data quality, documentation, portfolio cases, and stakeholder empathy.
QA to ML and Data Engineering
QA-to-ML and data engineering transition notes grounded in podcast examples on testing discipline, projects, cloud practice, and interviews.
Researcher to Data Science
How researchers and PhDs translate academic data work into data science, applied ML, data engineering, and research software roles.
Services to Product Founder
How consultants and freelancers turn repeated data problems into reusable products, open-source tools, or startup paths.
Software Engineer to ML
A transition path for software engineers moving into machine learning through project work, ML evaluation, production systems, MLOps, and role targeting.