Senior Data Engineer

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.