Data Engineer

Jellyvision•Chicago, IL

10h•$95,000 - $110,000•Remote

About The Position

Data Engineer I Who we are Jellyvision ALEX® , is on a mission to improve lives by helping people choose and use their benefits. We are raising the bar—for benefits and the employee experience (for our employees and those of the customers we serve) – by scaling personalization, compassion and an earnest intent to be helpful in all that we do. Jellyvision people are a group of creative problem solvers who use good judgment, give each other honest feedback, engage in real debate, and snack frequently. We are curious, hungry, and humble—because we know this is how we’ll continue to make an impact. We’re kind, biased towards action, and sweat the details to create great experiences for those we serve. We are an inclusive, human-first workplace. Respect and trust for each other are foundational, and our equitable total rewards offerings support the lives and holistic well-being of our unique people. At Jellyvision, expect career experiences that challenge you, empower you to have a direct impact on our mission, and enable you to learn, try, and do while having fun along the way. What’s the role? 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.

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/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

Nice To Haves

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

Responsibilities

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