Key Responsibilities
Data Modeling and Architecture
Lead the design of the Integrated Data Package (IDP) data model, covering multi-modal study assets including DICOM imaging, omics, and real-world data sources
Define the two-layer data architecture: operational relational layer for study metadata, cataloging, and access registry; lakehouse layer for versioned study assets at scale
Design schemas, partitioning strategies, and table formats across relational (PostgreSQL) and open table format (Apache Iceberg) layers to support both transactional and analytical access patterns
Establish cross-modal patient and study linkage standards, including integration with the Global Unique Patient Record Identifier (GUPRI) and related master data entities
Define data versioning and snapshot strategies for study-level packages, enabling reproducible dataset construction for algorithm development and regulatory submissions
Lakehouse and Query Layer
Architect the Apache Iceberg-based lakehouse layer on S3, including table design, schema evolution governance, compaction policies, and metadata management
Design the version catalog architecture using Project Nessie or equivalent catalog tooling, covering namespace structure, branching strategy, and atomic snapshot tagging
Define query access patterns and optimization strategies across the lakehouse layer using distributed SQL query engines
Govern the data access API surface exposed to downstream consumers including the algorithm development workbench and reporting services
FAIRification and Data Governance
Design proactive FAIRification pipelines that enrich incoming study data with standardized metadata, controlled vocabularies, and linkage keys at ingestion time
Define data quality validation rules, error handling workflows, and observability hooks across the ingestion and enrichment pipeline
Establish data lineage and provenance tracking across the full data lifecycle from ingestion through version snapshot to analytical consumption
Ensure data architecture supports GxP audit trail requirements including ALCOA+ principles for traceability, integrity, and contemporaneity
Stakeholder Collaboration and Governance
Serve as the primary data architecture authority for the program, partnering with imaging platform, workbench, and regulatory workstreams on cross-cutting data decisions
Engage directly with client data, engineering, and architecture stakeholders to align on data models, access patterns, and governance standards
Produce and maintain architecture artifacts including data models, schema documentation, ADRs, and data dictionary
Contribute to milestone delivery planning, technical risk management, and program-level architecture reviews
Required Qualifications
10+ years of experience in data architecture, data engineering, or enterprise data platform design
Expert-level proficiency in relational data modeling (PostgreSQL or equivalent), including schema design, normalization, JSONB/semi-structured patterns, and query optimization
Hands-on experience designing and operating modern lakehouse architectures using Apache Iceberg or equivalent open table formats (Delta Lake, Apache Hudi)
Strong background in distributed query engines (Presto, Trino, Spark SQL, or equivalent) and large-scale data partitioning strategies
Experience with data versioning concepts including snapshot isolation, time travel, schema evolution, and catalog management
Demonstrated experience delivering data platforms in regulated environments with GxP, 21 CFR Part 11, or equivalent compliance requirements
Strong written and verbal communication skills, with the ability to document data models and architecture decisions for mixed technical and regulatory audiences
Nice to Have
Hands-on experience with Project Nessie or equivalent transactional catalog tooling for Iceberg
Background in medical imaging data (DICOM) or multi-modal clinical data integration including omics or real-world data
Familiarity with FAIR data principles and their application to life sciences data platforms
Experience with workflow orchestration tools (Apache Airflow, Temporal, or equivalent) in the context of data pipeline design
Prior experience in a fixed-fee, milestone-based delivery engagement within a large regulated enterprise environment
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