Senior Data Architect, Integrated Data Platform

Key ResponsibilitiesData 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 submissionsLakehouse 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 servicesFAIRification 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 contemporaneityStakeholder 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 reviewsRequired 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 audiencesNice 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