Senior Data Engineer

HEALTH UNION LLC•Philadelphia, PA

11d•$95,000 - $131,000•Hybrid

About The Position

Health Union is hiring a Senior Data Engineer to build and evolve the data pipelines and platform capabilities that support its enterprise data environment. The role reports to the Senior Director of Enterprise Data Platforms and works closely with data engineering pod leads and team members. The engineer will execute the technical direction and roadmap established by data leadership, designing and building robust pipelines, implementing data models, and ensuring the reliability, quality, and compliance of delivered data. The position requires strong hands-on expertise with Snowflake and core AWS data services. It also uses dbt, Python, and advanced SQL, with a focus on performance tuning, optimization, and data validation. The role partners with analytics, software engineering, product, and business stakeholders and contributes to planning, code reviews, platform automation, and continuous improvement. This is a hybrid role based in Center City, Philadelphia, requiring at least two days in the office per week. Applicants must be legally eligible to work in the U.S. for any employer. Successful candidates must pass the E-Verify process after hire.

Requirements

At least 5 years of hands-on data engineering experience.
Strong, recent production experience with Snowflake and core AWS data services as a primary platform; examples include S3, Lambda, MWAA/Apache Airflow, and streaming or ingestion services such as Kinesis or Snowpipe. Depth in a solid subset is emphasized over breadth.
Working knowledge of dbt for data modeling and transformation, Python, and advanced SQL, including stored procedures and analytic functions, or demonstrated ability to ramp up quickly.
Strong knowledge of modern data engineering and data warehousing concepts, including relational and non-relational databases, data streams, and file stores.
Experience with performance tuning, query optimization, data profiling, and validation.
Experience building and maintaining reliable, well-tested data pipelines, with sound QA, testing, requirements-gathering, and documentation practices.
Understanding of data security, governance, and compliance principles as they apply to data platforms.
Strong communication and teamwork skills, with the ability to work across engineering, analytics, product, and business partners.
Self-motivated and proactive, with strong logical reasoning, a love for learning, and eagerness to grow skills and take on additional responsibilities.
Legally eligible to work in the U.S. for any employer.

Nice To Haves

Experience implementing CI/CD practices for data pipelines.
Openness to learning and adopting AI-driven data platform practices and agentic workflows.
Experience in healthcare, ad tech, programmatic audiences, or related industries.
Familiarity with clean-room environments and integrations, and adtech partners such as SSPs and DSPs.
AWS and/or Snowflake certifications, such as AWS Certified Data Engineer Associate or SnowPro.

Responsibilities

Design, build, and maintain scalable data pipelines and ETL/ELT processes using AWS, Snowflake, Python, and dbt, from ingestion through transformation and delivery.
Translate functional requirements into technical designs and high-quality code, including unit tests and documentation.
Implement and evolve data models and transformation layers using dbt on Snowflake, following data leadership’s standards and technical direction.
Optimize data workflows for performance, scalability, cost, and reliability.
Protect data accuracy and integrity through analysis, testing, profiling, and problem resolution.
Support data security, governance, and privacy practices and compliance with company policies.
Partner with analytics, software engineering, product, and business stakeholders to understand data needs and deliver maintainable solutions.
Participate in code reviews and share knowledge with peers to maintain engineering quality.
Contribute to quarterly and sprint planning for data engineering activities and communicate progress and trade-offs across project teams.
Automate and improve data platform processes to enhance efficiency and output.
Stay current with data engineering technologies and best practices and bring ideas to the team.