Job description

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.  Technical Skills and Keywords. 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. #LI-YG1 #LI-Hybrid The base pay salary range for this full-time position for candidates working onsite at our headquarters in Redwood City, CA is listed below. The range displayed on each job posting is intended to be the base pay salary range for an individual working onsite in Redwood City and will be adjusted for the local market a candidate is based in. Our base pay salary ranges are determined by role, level, and location. Individual base pay salary is determined by multiple factors, including job-related skills, experience, market dynamics, and relevant education or training. Please note that base pay salary range is one part of the overall total rewards program at RevMed, which includes competitive cash compensation, robust equity awards, strong benefits, and significant learning and development opportunities. 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. Revolution Medicines takes protection and security of personal data very seriously and respects your right to privacy while using our website and when contacting us by email or phone. We will only collect, process and use any personal data that you provide to us in accordance with our CCPA Notice and Privacy Policy . For additional information, please contact privacy@revmed.com . Base Pay Salary Range $120,000 — $150,000 USD We are aware of recent recruitment scams in which individuals or organizations falsely represent themselves as being affiliated with Revolution Medicines. These scams may appear as false job advertisements or unsolicited contacts through communication or chat platforms, email, phone, or text message. Please note that Revolution Medicines does not extend unsolicited employment offers and will never ask candidates to provide financial information, purchase equipment, or pay fees as part of the hiring process. All legitimate communication from Revolution Medicines will come from an official @revmed.com email address. If you believe you’ve been contacted by someone impersonating a Revolution Medicines recruiter, please report it to careers@revmed.com so we can share these impersonations with our IT team for tracking and awareness.

Requirements

Must-haves

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

Nice-to-haves

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.
Technical Skills and Keywords.
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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aipythonsqlmlmachine learningci/cd

Platform: greenhouse
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