RxSense - Senior Data Engineer

Remote - USA1w ago

Requirements

• 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.

Responsibilities

• Data 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.