Career Path Builder
Data Career Roadmap
A complete roadmap for Data Engineers and Data Scientists -- covering skills, tools, and progression from beginner to expert.
Beginner → Expert
Data Engineering
Data Science
SQL
Python
ETL · ML
1. Overview: Data Engineer vs Data Scientist
Data Engineer
Builds and maintains data pipelines, warehouses, and infrastructure.
- ETL/ELT pipelines
- Data modeling
- Data quality & governance
- Batch + streaming systems
Data Scientist
Analyzes data, builds ML models, and drives business insights.
- Exploratory data analysis
- Statistical modeling
- Machine learning
- Experimentation & A/B testing
2. Beginner Stage
Core Foundations (Both Roles)
- Python basics (variables, loops, functions)
- SQL fundamentals (SELECT, JOIN, GROUP BY)
- Data cleaning with Pandas
- Basic statistics (mean, variance, correlation)
- Excel/Sheets for quick analysis
Data Engineer Focus
- Understanding databases (OLTP vs OLAP)
- Intro to ETL concepts
- File formats: CSV, JSON, Parquet
Data Scientist Focus
- Exploratory Data Analysis (EDA)
- Visualization (Matplotlib, Seaborn)
- Intro to ML (train/test split, basic models)
Data Engineer Focus
- Advanced SQL (window functions, CTEs)
- Data modeling (Star/Snowflake schema)
- ETL/ELT tools (Airflow, dbt)
- Big data frameworks (Spark)
- Cloud data warehouses (Snowflake, BigQuery, Redshift)
Data Scientist Focus
- Supervised ML (regression, classification)
- Feature engineering
- Model evaluation (precision, recall, ROC‑AUC)
- Experimentation & A/B testing
- ML pipelines (Scikit‑learn)
4. Advanced Stage
Data Engineer Focus
- Streaming systems (Kafka, Kinesis)
- Data lake architecture
- Orchestration at scale
- Performance tuning (SQL + Spark)
- Data governance & lineage
Data Scientist Focus
- Advanced ML (XGBoost, LightGBM)
- Time‑series forecasting
- Unsupervised learning (clustering, PCA)
- Model deployment basics
- ML Ops collaboration
5. Expert Stage
Data Engineer Focus
- Distributed systems design
- Real‑time analytics
- Enterprise‑scale data platforms
- Security, compliance, and governance
Data Scientist Focus
- Deep learning for tabular/time‑series
- Advanced experimentation frameworks
- End‑to‑end ML system design
- Cross‑functional leadership
Data Engineer Tools
- Spark, Airflow, Kafka
- Snowflake, BigQuery, Redshift
- dbt, Databricks
- Docker, Kubernetes
Data Scientist Tools
- Pandas, NumPy, Scikit‑learn
- Jupyter, VS Code
- MLflow, Weights & Biases
- Tableau, Power BI
7. Portfolio Projects
Data Engineer Projects
- Build an end‑to‑end ETL pipeline
- Design a data warehouse schema
- Create a streaming pipeline with Kafka
- Optimize a large SQL workload
Data Scientist Projects
- Customer churn prediction
- Sales forecasting
- Fraud detection
- Experimentation analysis dashboard