• Act as a hands-on technical lead who not only defines the architecture but also codes, deploys, and maintains scalable ETL pipelines and data structures • Spearhead the technical implementation of the Translational Data Lake data ingestion, managing the ingestion of complex datasets (genomics, proteomics, imaging, lab data, etc.) into modern cloud architectures • Lead data engineering projects beyond the Data Lake, designing bespoke integration solutions for diverse scientific data sources across the Research organization • Design and script automated procedures to normalize unformatted data from external vendors (CROs) into a structured Common Data Model (CDM) • Partner with various functions in Research and IT to align infrastructure with scientific needs, ensuring solutions are robust, FAIR-compliant, and scalable • Develop and communicate the technical vision for biomarker data integration and reuse • Architect and implement scalable ETL procedures, APIs and front-end tools for data access and visualization • Engage stakeholders to gather requirements and incorporate feedback into design • Lead user acceptance testing (UAT) and ensure high-quality deliverables • Collaborate with IT and Translational leads to align infrastructure and governance processes • Champion FAIR principles and interoperability across translational and clinical programs

• Design and maintain data architecture and pipelines that enable the transformation of raw data into high-quality datasets for reporting, analytics, and business decision-making. • Collaborate closely with the Data Engineering team, analysts, and key stakeholders to ensure that data is accessible, accurate, and well-governed across the organization. • Design, build, and maintain robust ELT/ETL pipelines. • Contribute to the adoption of reproducible, version-controlled pipeline development practices. • Develop and optimize data warehouse models to support analytics use cases, ensuring performance, scalability, and usability. • Implement automated testing, monitoring, and data quality controls to identify and resolve issues proactively. • Improve storage efficiency and query performance to support scalability and effective cloud cost management (FinOps). • Work with data analysts and business stakeholders to understand requirements and ensure timely and reliable data availability for reporting, analytics, and machine learning use cases.

• Architect and develop scalable data pipelines and models using Snowflake and dbt • Design and operate orchestration using Airflow, Dagster, Prefect, or similar • Define modeling standards, storage patterns, and performance strategies • Collaborate on backend APIs, services, integration layers, and limited frontend interfaces supporting data-driven features • Contribute to CI/CD, environment management, and automated pipeline delivery • Implement quality checks, lineage, SLAs, and observability • Mentor engineers and drive architectural best practices • Participate in system design and multi-team architectural planning

• Translate SAP table structures and business processes (SD, MM, FI, O2C, P2P) into scalable data models • Design and develop robust ETL/ELT pipelines to replicate data from SAP S/4HANA, ECC, and other sources into BigQuery • Implement data warehousing best practices and advanced SQL transformations (Stored Procedures, Table Functions, Window Functions) • Develop and manage Slowly Changing Dimensions (SCD), Change Data Capture (CDC), and entity resolution strategies • Optimize queries using partitioning, clustering, and performance tuning techniques • Manage data replication tools and pipeline orchestration processes • Ensure data quality, governance, consistency, and scalability across systems