Onsite in Boston, BioNTech offers an opportunity to lead data engineering at the intersection of science and medicine. In this Associate Director Data, Digital & Informatics (Senior Data Engineer) role, you will design, deliver, and maintain scalable, enterprise-grade data pipelines on Spark, Databricks, Delta Lake, and AWS to support analytical, scientific, clinical, and operational decision making across the R&D lifecycle. The position carries a competitive salary range of USD 146,300 - 234,100 per yearly and provides a path to influence platform strategy, governance, and collaboration across cross-functional teams. You will mentor engineers, partner with Platform, Cloud, and DevOps teams, and work with CROs and external vendors to advance data capabilities in a regulated, data-driven environment.
Responsibilities
- Design, build, maintain, and optimize scalable, secure, and resilient data pipelines using Spark, Databricks, Delta Lake, and AWS.
- Coordinate data flow across clinical systems (EDC, CTMS, IRT, Rave, biosample systems, BRIMS, PV, MDM) and implement transformations aligned to CDISC standards (CDASH, ODM, SDTM, ADaM).
- Establish data quality, testing, monitoring, and performance frameworks; perform clinical data cleaning, reconciliation, validation, and QC.
- Collaborate with Platform, Cloud, and DevOps teams to evolve the clinical data platform; own CI/CD automation, infrastructure-as-code, observability, data lineage, and pipeline monitoring.
- Lead cross-functional data engineering programs as a senior engineering leader; mentor engineers on best practices, coding standards, automation, and reproducibility.
- Integrate data across R&D domains including translational science, clinical development, safety, regulatory, and real-world evidence, with API-based ingestion (REST, GraphQL) and metadata management.
- Collaborate with QA, Data Analysts, Data Scientists, Cloud Ops, Biostatistics, Clinical Ops, CROs, and external vendors to foster an open, inclusive team culture.
- Engage governance, compliance, and security to ensure alignment with GxP, CSV, FAIR, privacy, and data ethics.
- Build and maintain data models, specifications, mapping documents, and QC documentation, contributing to enterprise data strategies and architecture roadmaps.
Requirements
- Bachelor’s or Master’s degree in computer science, engineering, a related field, or equivalent practical experience.
- 6 to 10+ years of data engineering experience with production-grade data pipelines and large-scale distributed systems.
- 2 to 6+ years of experience with Databricks and AWS, plus ETL/ELT tools and cloud data lake/warehouse solutions.
- Experience in biotech/pharma regulated or complex scientific environments, including GxP/CSV and data governance (privacy, data ethics).
- Strong SQL, PySpark, and Python skills; proficiency with SAS and R; experience with CI/CD, DevOps (GitHub), infra-automation (Terraform), data modeling, metadata management, and data quality frameworks.
- Hands-ons experience with clinical data standards and systems (CDISC, EDC, CTMS, IRT, Rave, biosample systems, BRIMS, PV, MDM) and data masking/blinded-unblinded workflows.
- Familiarity with ML/AI workloads and model-ready data engineering; experience across translational science, clinical development, safety, regulatory, and real-world evidence domains.
- Experience working with CROs and external vendors; strong analytical, problem-solving, communication, and cross-functional partnership skills.
- A customer-oriented and agile mindset, with the ability to manage competing priorities and to work effectively with people from diverse disciplines, cultures, and backgrounds.
Technologies
- Spark, Databricks, Delta Lake, AWS
- REST, GraphQL
- GitHub, Terraform
- SAS, R, Python, PySpark, SQL
- Rave, CTMS, EDC, IRT, BRIMS, PV, MDM
How to apply
Apply now by submitting your documents through our online form. Include your Curriculum Vitae, a copy of your ID, copies of degree certificates and professional certificates, a motivation letter, and your contact details.
