Note: The job is a remote job and is open to candidates in USA. RxSense is hiring a Senior Data Engineer to own its data platform end to end, including cloud infrastructure, data pipelines, orchestration, and governed data layers. The role builds and maintains Snowflake-based systems and models that serve analytics, reporting, and AI consumers.
Responsibilities
- 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
- 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
- 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
- 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
- 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
Skills
- 5+ years of hands-on experience in data engineering — building pipelines, data models, and platform infrastructure in a data-driven organization
- Proven, hands-on production experience building data models and transformations in Snowflake. Deep familiarity with Snowflake architecture, performance optimization, and cost management
- 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
- 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
- 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
Company Overview
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