Data Engineer

2+ years of practical experience (professional, internship, and bootcamps count) Solid Python and working SQL proficiency Familiarity with cloud platforms (AWS/GCP/Azure) and modern data tooling (e.g Airflow, dbt, Spark, Snowflake, Databricks, Redshift) Understanding of data modeling concepts (e.g., star schema, normalization) and ETL/ELT design practices Experience reading/writing Parquet Ability to write and run basic Airflow DAGs Docker fundamentals Git fundamentals, agile development and CI/CD practices Demonstrated curiosity Genuine tinkerer energy: you’ve built personal data projects for fun, tried random tools (Polars, DuckDB, Ollama, local LLMs, MotherDuck, etc.), and probably have a messy but awesome docker-compose.yml on your machine

Build, test, and deploy Airflow DAGs in MWAA Write clean, production-ready Parquet files using PyArrow or Spark Implement ETL/ELT pipelines Maintain S3 storage structures Assist with dimensional modeling Implement basic data quality checks using Python Troubleshoot failing DAGs and respond to alerts Participate in on-call support rotation Update and maintain pipeline documentation Participate in code reviews Partner with Senior Data Engineers and Analytics Engineers on architecture alignment Work with analytics and product teams to understand and deliver data requirements Document troubleshooting procedures and platform patterns Contribute to sprint planning and technical discussions