Source description
About the role
2+ years in data engineering or analytics engineering with proven ability to build pipelines and scalable workflows.
Strong SQL skills for querying large, complex datasets.
Proficiency in Python for data engineering tasks (transformations, APIs, automation).
Experience with cloud data warehouses and storage (GCP preferred: BigQuery, Cloud Storage, Composer; AWS/Azure equivalents acceptable).
Hands on experience with dbt or similar data modeling tools.
Comfort working in collaborative dev/staging/prod environments, partnering with Product and Tech to safely test, launch, and anticipate the impact of new changes.
Curiosity about operational workflows and a drive to partner with non-technical teams, ensuring data and reporting align with how the business actually runs. You're not just a spec-taker, you're part of the solution.
A proactive, problem-solving mindset and ability to thrive in fast-paced, iterative environments.
Strong communication skills to collaborate with analysts, engineers, and business stakeholders.
Nice to have:
Knowledge of healthcare data (claims, ADT feeds, eligibility files).
Familiarity with Git/GitHub for version control.
Early-stage startup experience (seed/Series A), especially mission-driven ones.
Experience building semantic layers and data models in Looker (LookML).
More at Empassion