Director Enterprise Data Platform

Responsibilities

• Build and operate the medallion data lake.
• Own the full medallion architecture — Raw, Silver, and Gold layers — on Google BigQuery.
• Define standards for how data is ingested, conformed, cleansed, and made available for downstream consumption.
• Ensure every layer is reliable, documented, and governed.
• Hands-On with a high-performing data engineering team.
• Develop and retain data engineers with deep expertise in cloud data platforms, pipeline development, and data modeling.
• Foster a culture of engineering rigor — where data quality, test coverage, and documentation are treated as first-class deliverables alongside throughput.
• Own source system onboarding.
• Drive the strategy and execution for integrating new source systems into the data fabric.
• Define and enforce data contracts with upstream source owners.
• Ensure ingestion pipelines are resilient, observable, and built to scale.
• Define and meet data SLAs.
• Establish and own SLA commitments for data freshness, completeness, and availability across all critical data domains.
• Build the observability and alerting infrastructure to detect and resolve pipeline failures before they impact downstream consumers or data products.
• Partner with Architecture and Semantic Intelligence.
• Work closely with the Architecture team to ensure the Gold layer is built to canonical data model specifications rather than product-specific one-offs.
• The Enterprise Data Platform team executes against architecture-approved designs — enforcing the platform's semantic gate and preventing technical debt accumulation.
• Drive cost efficiency.
• Own BigQuery compute and storage cost management for the data fabric.
• Identify optimization opportunities — query efficiency, partitioning strategy, materialized view usage — and implement a continuous improvement practice around platform economics.
• Contribute to platform strategy.
• Participate as a senior leader in the Enterprise Data Platform leadership team.
• Bring the perspective of the data foundation into roadmap planning, organizational decisions, and executive reporting through the VP of Enterprise Data Platform.

  Qualifications

  • Bachelor’s degree in computer science, engineering, information systems, or related technical field.
  • 10+ years of progressive experience in data engineering, cloud engineering, data infrastructure, or distributed systems engineering roles.
  • 5+ years of leadership experience managing engineering teams responsible for large-scale enterprise platforms.
  • Strong experience designing and operating cloud-native platforms, preferably on Google Cloud Platform.
  • Proven experience designing and implementing medallion or multi-layer data lake architectures (Raw/Bronze, Silver, Gold) at enterprise scale.
  • Deep expertise with modern engineering practices including infrastructure-as-code, CI/CD, observability, automation, resiliency engineering, and platform operations.
  • Strong command of SQL-based transformation patterns, pipeline orchestration (cloud composer / Airflow or equivalent).
  • Experience defining and enforcing data contracts, SLAs, and quality standards across complex, multi-source data environments.
  • Experience implementing platform security controls, identity integration, tokenization, secrets management, and secure data access patterns.
  • Demonstrated ability to lead engineering teams through large-scale platform modernization and transformation initiatives.
  • Strong communication and collaboration skills with the ability to partner effectively across engineering, analytics, architecture, and business teams.

    Preferred Qualifications

    • Master’s degree in computer science, engineering, business administration, or related field.
    • Experience in healthcare, life sciences, or other highly regulated industries.
    • Familiarity with modern data platform technologies including BigQuery, Airflow, Looker, Terraform, and observability platforms.
    • Experience implementing platform capabilities supporting synthetic data, data observability, metadata management, and data trust frameworks.