Sr. Director, Data Engineering & Architecture

Role Summary

The Senior Director, Data Engineering & Architecture will be responsible for shaping and implementing the organization's comprehensive data platform strategy. This role will provide leadership in data architecture and engineering best practices while driving operational excellence to facilitate data-driven insights across pre-clinical, clinical and G&A functions. Additionally, the position will support Structure’s growing AI initiatives. The Senior Director will ensure that essential data is accessible, reproducible, and actionable, thereby accelerating AI-enabled drug discovery efforts. Strategic direction and technical oversight will be provided, with a strong emphasis on robust data governance, pipeline engineering, and seamless interoperability of data across scientific teams.

Responsibilities

• 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.
• Align with Structure’s enterprise AI strategy to enable predictive modeling, translational research platforms, and intelligent automation across functions.
• 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.

Qualifications

• 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.
• Track record in leading cross-functional teams and transformations of data architecture in complex, matrixed environments.
• 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).

Preferred Attributes

• Biotech/pharma experience, especially with scientific/clinical datasets.
• Ability to speak Mandarin
• Understanding of drug development (pre-clinical and clinical) a plus
• Familiarity with FAIR principles, translational research data interoperability.
• Track record in AI/ML enabling data infrastructure (feature stores, model pipelines).
• Publications, contributions, or thought leadership in enterprise data architecture or AI in biotherapeutics.

Education

• Advanced degree (MS or PhD) preferred in related field.