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Engineering Roadmap
Data & ML Engineer Roadmap
Data and ML engineering covers everything from raw data to deployed models. This roadmap is for builders, not researchers the goal is to ship reliable systems, not to write papers.
7 stages 6-12 months Intermediate Engineers building data pipelines and ML-powered products
By the end you’ll be able to
- 1Build reliable, observable data pipelines
- 2Train, evaluate, and deploy classical ML models
- 3Run production ML services with monitoring
- 4Integrate LLMs alongside traditional ML
- 5Reason about feature stores, drift, and retraining
01
Stage 1 4-6 weeks
Python, Pandas, SQL
The data triad
Pandas (or Polars) for in-memory wrangling, SQL for everything in a warehouse, Python as the glue. You'll use all three every day.
Skills to develop
Python data idiomsPandas / PolarsAdvanced SQL (window funcs, CTEs)Jupyter / VS Code workflow
02
Stage 2 4-6 weeks
Statistics & ML Fundamentals
Just enough to be dangerous
Probability, distributions, regression, classification, evaluation metrics. You don't need calculus from scratch you need to read sklearn and Hugging Face docs without confusion.
Skills to develop
Probability distributionsLinear / logistic regressionDecision trees, random forests, XGBoostTrain/val/test splitsEvaluation metrics by task
Resources
- VideoStatQuest by Josh StarmerFREE
- BookHands-On Machine Learning by Aurélien Géron
03
Stage 3 3-5 weeks
Data Pipelines
dbt, Airflow, Dagster, Prefect
Modern data eng is built around dbt for transformations and an orchestrator (Airflow / Dagster / Prefect) for scheduling. Lineage, tests, and observability matter as much as the SQL.
Skills to develop
dbt models, tests, docsAirflow / Dagster / Prefect DAGsIdempotent transformationsData quality testingSchema evolution
Resources
04
Stage 4 3-4 weeks
Warehouses & Lakes
BigQuery, Snowflake, DuckDB, Iceberg
BigQuery, Snowflake, and Redshift dominate enterprise. DuckDB and ClickHouse are excellent for cheaper analytics. Iceberg / Delta tables let you separate storage from compute properly.
Skills to develop
BigQuery / Snowflake basicsDuckDB for local analyticsIceberg / Delta table formatCost & query optimization
05
Stage 5 6-10 weeks
Production ML
Training, serving, monitoring
Pick a stack: PyTorch + Hugging Face for deep learning, sklearn + XGBoost for tabular. Track experiments with W&B or MLflow. Serve with vLLM, Triton, or BentoML. Monitor for drift.
Skills to develop
PyTorch / Hugging FaceMLflow / Weights & BiasesvLLM / Triton / BentoML for servingFeature stores (Feast)Drift monitoring
Recommended tools
06
Stage 6 3-5 weeks
LLMs in the ML Stack
RAG, fine-tuning, evals
Even classical ML teams now have LLM workloads. Learn fine-tuning (LoRA / QLoRA), RAG architecture, and how to evaluate generative outputs systematically.
Skills to develop
LoRA / QLoRA fine-tuningRAG architecturesLLM evals (Ragas, Inspect)Cost-aware inference
Recommended tools
Resources
07
Stage 7 2-4 weeks
Streaming & Real-Time
Kafka, Flink, Materialize
When batch isn't enough. Kafka for the substrate, Flink or Materialize for stateful streaming. Most products don't need this but when they do, nothing else works.
Skills to develop
Kafka producers / consumersStreaming joinsExactly-once semanticsMaterialized views
Ready to build with what you've learned?
We pair these roadmaps with hands-on engagements pair-programming, code review, and architecture support.