Senior Data Engineer

Revolution Medicines•Redwood City, CA

3d•$186,000 - $233,000•Onsite

About The Position

Revolution Medicines is 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.

Requirements

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 the 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.

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.

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.