Senior Data Engineer

Revolution Medicines is a late-stage clinical oncology company developing novel targeted therapies for patients with RAS-addicted cancers. The companys R&D pipeline comprises RAS(ON) inhibitors designed to suppress diverse oncogenic variants of RAS proteins. The companys 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 aSenior Data Engineer you will play a key role in designing building andoperatingtrusted 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 role is highly hands-on and cross-functional. You will not be limited to one business domain; instead you will helpestablishconsistent 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 andoperatescalable data pipelines using modern cloud data platform technologies with a strong emphasis onDatabricksPythonSQL andDBT.

• 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 asmedallion architecturestar 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 engineeringdelivery.

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 establishDataOpspractices that allow analytics AI ML and business intelligence use cases to move safely from prototype to production.

Cross-Functional Collaboration

• Partner heavily withInformation 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.

• Bachelors degree in Computer Science Engineering Data Science Information Systems or a related field or equivalent professional hands-on experience building production-grade data pipelines usingPythonandSQL.

• Experience withDatabricks Spark Delta Lake Lakehouse architecture or equivalent modern data platform technologies.

• Practical experience withDBTor 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 AirflowDatabricks WorkflowsDagster 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 SQLDBT 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

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 @ email address.

If you believe youve been contacted by someone impersonating a Revolution Medicines recruiter please report it to so we can share these impersonations with our IT team for tracking and awareness.