Define and execute a unified enterprise data strategy aligned with corporate objectives across the full Structure ecosystem (pre‑clinical, clinical, G&A)
Lead modernization of data platforms—cloud data lakes/warehouses, data mesh or lakehouse architectures, master data solutions, and advanced analytics infrastructure
Establish data governance frameworks encompassing privacy, compliance (e.g., GxP, 21 CFR Part 11, HIPAA), and high data quality standards
Architect scalable, secure, and high-performance infrastructures for structured and unstructured data with clear data models, taxonomies, and semantic layers
Lead implementation of robust ETL pipelines, real-time ingestion (streaming), integration, and transformation processes across internal and external sources
Establish data governance with legal, compliance, and business teams
Enable data cataloging, lineage, and metadata management for easy discovery and tracking
Apply CI/CD, observability, monitoring, and reliability to data pipelines in regulated environments
Ensure operational excellence through robust SLAs, effective incident management, and scalable performance
Collaborate with R&D, Clinical, Commercial, and G&A teams to deliver tailored data solutions supporting scientific insight, clinical analytics, commercial intelligence, and administrative decision-making
Facilitate self-service analytics and data democratization initiatives
Integrate data platform capabilities to support AI/ML lifecycle—data provisioning, ML Ops, model feature stores, deployment, and monitoring
Build, mentor, and inspire a high-performing team of architects, engineers, and operations professionals
Cultivate a data-driven culture of innovation, quality, accountability, and cross-functional collaboration

Requirements

Advanced degree (MS, PhD preferred) in Computer Science, Data Science, Engineering, Bioinformatics, or related field
10+ years in data engineering/architecture roles, with at least 5+ years leading enterprise-scale platforms in biotech, pharma, healthcare, or regulated industries
Expertise with modern data architectures (data lakes, warehouses, mesh, lakehouses), cloud platforms, and modernization strategies
Hands-on experience with ETL/ELT, streaming ingestion, data modeling, and platform performance optimization
Familiarity with CI/CD, observability, ML Ops tooling for scalable and compliant operations
Experience with modern cloud-based data platforms (e.g., Snowflake, Databricks, AWS/GCP/Azure)
Deep understanding of data governance, MDM, and integration best practices
Ability to influence senior executives, drive cross-functional alignment, and lead through ambiguity
Strong stakeholder management: able to translate business needs into technical delivery and gain alignment across functions
Excellent verbal and written communication skills
Deep understanding of regulated data processes—data integrity, auditability, and compliance (GxP, HIPAA, GDPR)

Benefits

bonus
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Applicant Tracking System Keywords

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Hard Skills & Tools

data strategydata governanceETLdata modelingcloud data lakesdata warehousesdata meshreal-time ingestionadvanced analyticsML Ops

Soft Skills

leadershipstakeholder managementcommunicationcross-functional collaborationinfluencementoringproblem-solvinginnovationaccountabilityadaptability