Senior Data Engineer

Revolution Medicines is a late-stage clinical oncology company developing novel targeted therapies for patients with RAS‑addicted cancers. The company’s R&D pipeline comprises RAS(ON) inhibitors designed to suppress diverse oncogenic variants of RAS proteins. The company’s RAS(ON) inhibitors daraxonrasib (RMC‑6236), a RAS(ON) multi‑selective inhibitor; elironrasib (RMC‑6291), a RAS(ON) G12C‑selective inhibitor; zoldonrasib (RMC‑9805), a RAS(ON) G12D‑selective inhibitor; and RMC‑5127, a RAS(ON) G12V‑selective inhibitor, are currently in clinical development.

As a new member of the Revolution Medicines team, you will join other outstanding professionals in a tireless commitment to patients with cancers harboring mutations in the RAS signaling pathway.

We are building a modern, scalable data and AI engineering foundation to accelerate insight generation across the enterprise, with a strong focus on R&D, business operations, and future digital product capabilities.

• Design, build, test, and operate scalable data pipelines using modern cloud data platform technologies, with a strong emphasis on Databricks, Python, SQL, and DBT.
• Develop curated, production‑grade datasets and data products that are reliable, discoverable, reusable, and aligned with business and scientific needs.
• Implement data modeling patterns such as medallion architecture, star schemas, dimensional models, roll‑up tables, semantic layers, and business intelligence‑ready data structures.
• Build pipelines that integrate data from enterprise applications, scientific systems, transactional systems, external sources, and domain‑specific platforms.
• Collaborate with Data Product Management and business stakeholders to translate data product requirements into robust technical designs.
• Contribute to reusable templates, frameworks, and engineering standards that improve consistency and speed across data engineering delivery.
• Implement automated data quality checks, validation rules, reconciliation logic, and exception handling across critical pipelines.
• Build monitoring and observability into data workflows, including pipeline health, freshness, completeness, accuracy, volume anomalies, lineage, and SLA/SLO tracking.
• Create operational dashboards, alerts, runbooks, and remediation processes to support reliable production data operations.
• Continuously improve pipeline performance, cost efficiency, maintainability, and reliability.
• Help establish Data Ops practices that allow analytics, AI, ML, and business intelligence use cases to move safely from prototype to production.
• Partner heavily with Information Sciences, R&D teams, business departments, platform engineering, security, privacy, and application owners to ensure data solutions integrate cleanly with enterprise systems and operating models.
• Work across multiple business and scientific domains to enable consistent, interoperable, and governed data pipelines and data products.
• Collaborate with R&D stakeholders to understand scientific and operational workflows, data dependencies, metadata needs, and analytical use cases.
• Help define and implement data contracts, integration patterns, source‑to‑target mappings, metadata standards, and stewardship practices.
• Promote a product‑minded engineering culture focused on business impact, trust, adoption, and operational ownership.

• 5+ years of professional experience in data engineering, analytics engineering, software engineering, or a related technical role.
• Strong hands‑on experience building production‑grade data pipelines using Python and SQL.
• Experience with Databricks, Spark, Delta Lake, Lakehouse architecture, or equivalent modern data platform technologies.
• Practical experience with DBT or similar transformation frameworks, including model design, testing, documentation, and deployment.
• Strong understanding of data modeling for analytics and business intelligence, including dimensional modeling, star schemas, roll‑ups, aggregates, semantic layers, and BI consumption patterns.
• Experience working with cloud data platforms and modern data and orchestration stacks.
• Strong communication…