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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Engineer - **Company:** Health Union - **Location:** Philadelphia, PA, United States - **Experience:** Expert - **Salary:** $95,000.0 - $131,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Unit Testing, Code Review, Continuous Delivery, Information Engineering, Extract Transform Load (ETL), Data Profiling, Data Security, Data Warehousing, Python (Programming Language), Automation of Marketing, Performance Tuning, Scrum Methodology, Query Optimization, Software Engineering, SQL Stored Procedures, Data Streaming, Enterprise Data Management, Sql Optimization, Snowflake, Analytic Functions, Agentic-AI, AWS Data Analytics, Non-relational Database, Data Management, Data Pipelines - **Published:** October 16, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=9b76829daec2c688 ## About the Role Strong hands-on expertise with Snowflake and core AWS data services is essential. Experience with dbt, Python, and advanced SQL is highly valued, along with solid performance-tuning, optimization, and data-validation skills., * 5+ years of hands-on data engineering experience, with strong, recent production experience on Snowflake and core AWS data services as the primary platform, for example S3, Lambda, MWAA/Apache Airflow for orchestration, and streaming/ingestion services such as Kinesis or Snowpipe. Familiarity with a solid subset is what matters; depth over breadth. * Working knowledge of dbt for data modeling and transformation, Python, and advanced SQL (stored procedures, analytic functions), or the demonstrated ability to ramp quickly. * Strong command of modern data engineering and data warehousing concepts, including relational and non-relational databases, data streams, and file stores, and of 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. * Experience implementing CI/CD practices for data pipelines is a plus. * Understanding of data security, governance, and compliance principles as they apply to data platforms. * Openness to learning and adopting AI-driven data platform practices and agentic workflows as this becomes a growing team focus; willingness to lean in matters more than extensive prior expertise. * Strong communication and teamwork skills, with the ability to work effectively across engineering, analytics, product, and business partners. * A self-motivated, proactive mindset, strong logical reasoning, and a love for learning, with an eagerness to keep growing your skills and take on more as opportunities arise. * Experience in healthcare, ad tech, programmatic audiences, or related industries is a plus. Familiarity with clean-room environments/integrations and adtech partners (SSPs, DSPs) would be a plus. * AWS and/or Snowflake certifications (e.g. AWS Certified Data Engineer Associate, SnowPro) are an advantage. ## Description As a Senior Data Engineer, you will help build and evolve the data pipelines and platform capabilities that power our enterprise data environment. Reporting to the Senior Director of Enterprise Data Platforms and working closely with our data engineering pod leads and team members, you will design and build robust data pipelines, implement data models, and ensure the reliability, quality, and compliance of the data we deliver. You will be a hands-on senior engineer who executes against the technical direction and roadmap set by our data leadership., * 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 clear technical designs and well-structured, high-quality code, complete with unit testing and documentation. * Implement and evolve data models and transformation layers (dbt on Snowflake), following the standards and technical direction set by data leadership. * Optimize data workflows for performance, scalability, cost, and reliability. * Safeguard the accuracy and integrity of our data through meticulous analysis, rigorous testing, data profiling, and problem resolution. * Support data security, governance, and privacy practices to ensure appropriate use of data and compliance with company policies. * Partner with analytics, software engineering, product, and business stakeholders to understand data needs and deliver sound, maintainable solutions. * Participate in code reviews and share knowledge with peers to help maintain a high bar for engineering quality. * Contribute to quarterly and sprint planning for Data Engineering activities, and communicate progress and trade-offs clearly across project teams. * Automate and improve aspects of our data platform to enhance efficiency and output. * Stay current with technologies and best practices in the data engineering space, and bring ideas back to the team.