Data Engineer, Jellyvision

• 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/ELTdesign 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

Desirable

• As a Data Engineer I, you'll help build and maintain the data infrastructure that enables our teams to personalize benefits for millions of users
• This is an early-career role focused on developing strong data engineering fundamentals - building pipelines, designing storage structures, and modeling data that power products, client analytics, and strategic decisions
• You'll work closely with the rest of our data team to deliver reliable, well-structured data across the organization
• 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
• 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