Build and maintain data pipelines and storage
Build, test, and deploy Airflow DAGs in MWAA following team standards
Write clean, production-ready Parquet files using PyArrow or Spark with proper compression and partitioning
Implement ETL/ELT pipelines from source → landing → transformed layers
Help maintain S3 storage structures following existing partitioning, lifecycle, and access policies
Assist with dimensional modeling—turn normalized data into star-schema facts and dimensions under senior guidance
Implement basic data quality checks using Python
Troubleshoot failing DAGs, rerun backfills, and respond to alerts
Participate in on-call support rotation
Update and maintain pipeline documentation
Learn, collaborate, and help grow our data platform
Participate in code reviews to learn best practices and improve code quality
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

Requirements

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.
Nice to Have:
Experience with AI coding assistants
Exposure to AI tools (MCP servers, Ollama, local LLMs)
Knowledge of Delta Lake or Snowflake clustering
Basic Terraform experience
Simple data-quality tooling

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

PythonSQLAirflowETLELTParquetDockerGitdata modelingdata quality checks

curiositycollaborationtroubleshootingcommunicationparticipation in code reviewssprint planningtechnical discussions