Position SummaryThe Senior Data Engineer owns the data platform end to end - designing, building, and maintaining the infrastructure, pipelines, and governed data layers that power analytics and downstream consumers across the organization. This is a hands-on engineering role centered on cloud data platform development, pipeline orchestration, and data modeling at scale.
The guiding principle is to build once, consume everywhere - define transformations and metrics a single time, governed and version-controlled in the warehouse, then serve them consistently to every downstream consumer: BI tools, client-facing reporting, and AI systems.
The ideal candidate is comfortable building data platforms and systems from scratch, thrive in a platform development and maintenance environment, and brings strong hands-on experience with Snowflake and workflow orchestration tools such as Apache Airflow and dbt (or equivalent platforms). This role does not own BI visualization - your work spans raw ingestion through the governed gold layer.
Key ResponsibilitiesData Platform Development & Maintenance- Design, build, and maintain scalable data pipelines and platform infrastructure on Snowflake.
- Architect and implement end-to-end data flows from source ingestion through the silver and gold layers.
- Own platform reliability, performance tuning, and ongoing maintenance - treating the data platform as a product.
- Build systems from the ground up where needed; evaluate existing systems and refactor for scale, cost efficiency, and maintainability.
- Establish engineering standards: version control, CI/CD, testing frameworks, and documentation for all platform components.
Pipeline Orchestration & Workflow Management- Design and maintain orchestration workflows using Apache Airflow (or equivalent) to schedule, monitor, and manage data pipelines.
- Ensure pipeline reliability, observability, and failure recovery through alerting, logging, and automated retries.
- Coordinate dependencies across upstream sources and downstream consumers with well-defined SLAs.
Gold-Layer Modeling & Metrics- Design, build, and maintain gold-layer data models and a governed metrics layer in Snowflake using dbt - clean, tested, documented, and version-controlled.
- Establish metric definitions as the single source of truth: calculated once, reused everywhere, with clear ownership and change management.
- Push transformation and calculation logic upstream into the warehouse so the consumption layer stays simple, consistent, and high-trust.
- Partner with analytics and BI stakeholders on the boundary between pipelines (data engineering) and consumption (analytics/reporting).
- Structure and document data models so they meet the governance and consistency requirements of AI agents and autonomous analytical systems.
AI-Native Development- Build with coding agents and AI-assisted tooling as a primary part of the workflow - accelerating pipeline development, data modeling, documentation, and testing.
- Independently set up and manage development environments, repositories, and the tooling needed to work effectively with agentic systems.
- Help establish team practices and standards for building data infrastructure in the AI era.
Quality, Governance & Platform Judgment- Champion data quality, consistency, and trust across all platform layers - ensuring data holds up under scrutiny from any downstream consumer.
- Contribute to platform architecture decisions; identify opportunities for automation, templating, and process improvement.
- Apply sound engineering judgment to evaluate build vs. buy decisions and tooling trade-offs.
QualificationsRequired- 5+ years of hands-on experience in data engineering - building pipelines, data models, and platform infrastructure in a data-driven organization.
- Snowflake:Proven, hands-on production experience building data models and transformations in Snowflake. Deep familiarity with Snowflake architecture, performance optimization, and cost management.
- Workflow Orchestration (must have):Hands-on experience with Apache Airflow or an equivalent DAG-based orchestration platform (e.g., Prefect, Dagster, Mage). Ability to design, deploy, and operate complex pipeline workflows.
- dbt:Demonstrated experience using dbt for transformation modeling, testing, and documentation in a production warehouse environment. Equivalent semantic layer tooling experience considered.
- Strong SQL proficiency - able to write, optimize, and review complex queries across large datasets.
- Experience building data platforms or systems from scratch - not just extending existing infrastructure but designing and standing up new ones.
- Comfort with platform development and maintenance as a long-term ownership model - not just project delivery.
- Fluency working in the AI era: building with coding agents, setting up development environments, and using AI tooling as a core productivity multiplier.
- Familiarity with software engineering practices: version control (Git), CI/CD pipelines, automated testing, and code review.
- Strong communication skills - able to translate business requirements into technical data platform decisions.
Preferred- Experience in pharmacy benefits management, healthcare technology, or a related regulated industry.
- Experience with cloud infrastructure and data platform tooling (AWS, Azure, or GCP) alongside the data stack.
- Familiarity with data governance frameworks, data contracts, or data mesh principles.
- Experience with streaming or near-real-time data pipelines (e.g., Kafka, Kinesis, Spark Streaming).
- Exposure to semantic/metrics layer tooling (e.g., MetricFlow, Cube, LookML).
- Experience building demo environments or client-facing data products.
Salary Range: 145,000 - 165,000
