Senior Data Engineer

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:

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.
• Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, or a related field, or equivalent professional experience.
• 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.

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.

Base Pay Salary Range

$120,000 - $150,000 USD