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. The Opportunity 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. Key Responsibilities 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 DataOps 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. Required Skills, Experience and Education 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 skills and ability to translate ambiguous business or scientific data needs into clear, scalable engineering solutions. Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or a related field, or equivalent professional experience. Preferred Skills Experience in life sciences, biotechnology, pharmaceutical R&D, clinical development, precision medicine, or another regulated data environment. Experience with data cataloging, metadata management, lineage, access controls, and stewardship workflows. Experience with workflow orchestration tools such as Airflow, Databricks, Workflows, Dagster or equivalent technologies. Experience supporting BI platforms such as Power BI, Tableau, Looker, or similar tools. Experience designing data products that support analytics, machine learning. Core technologies: Databricks, Python, SQL, DBT, Spark, Delta Lake. Data architecture: Lakehouse, medallion architecture, dimensional modeling, star schema, semantic layer, data marts, roll‑up cubes, curated datasets. Data operations: Data quality, data observability, lineage, metadata, CI/CD, automated testing, orchestration, monitoring, alerting, incident response. Integration: APIs, data contracts, batch and streaming pipelines. Benefits Base pay salary range for this full‑time position for candidates working onsite at our headquarters in Redwood City, CA is listed below. Base Pay Salary Range: $186,000 — $233,000 USD Revolution Medicines is an equal opportunity employer and prohibits unlawful discrimination based on race, color, religion, gender, sexual orientation, gender identity/expression, national origin/ancestry, age, disability, marital status, medical condition, and veteran status. #J-18808-Ljbffr Revolution Medicines