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.

As a Senior Data Engineer, you will play a key role in designing, building, and operating trusted data pipelines, curated data products, and reusable engineering patterns across domains. You will work closely with Data Product Management, Information Sciences, R D, business stakeholders, analytics teams, platform engineers, and application owners to turn complex data from enterprise systems into reliable, governed, and usable data assets.

This role is highly hands-on and cross-functional. You will not be limited to one business domain; instead, you will help establish consistent data engineering practices across multiple areas, enabling cohesive data products, scalable pipelines, high data quality, and better decision-making across the organization.

For example, the data products you build may support trial enrollment and site-activation tracking, cross-study views across RAS(ON) programs, biomarker/genomic cohort analyses, safety and efficacy reporting, translational assay integration, portfolio planning, and AI-ready datasets for scientific decision-making.

Key Responsibilities include:

Data Engineering and Data Products

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.

Data Quality, Automation, and Observability

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.

Cross-Functional Collaboration

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 technolo…