Director, Data Platform Engineering

Responsibilities

• Hands-On Platform Architecture & Engineering
  • Design, build, and own the end-to-end architecture of Waters' Enterprise Data Platform: Databricks Lakehouse, Power BI semantic layer, and SAP integration touchpoints.
  • Personally lead Infrastructure as Code implementation in Terraform: workspaces, Unity Catalog objects, compute policies, access bindings, and CI/CD pipelines.
  • Write the modules, not just approve them.
  • Architect and enforce Unity Catalog governance: fine-grained permissions, data classification, lineage, row and column-level security, and audit controls for a regulated life-sciences environment.
  • Drive Delta Lake architecture decisions, cluster optimization, job orchestration patterns, and platform observability across development, staging, and production environments.
  • Partner closely with data engineering, analytics, and data science teams to ensure compute environments are stable, performant, and right-sized for their workloads.
• Power BI & Analytics Platform Engineering
  • Build and govern a high-performance Power BI enterprise environment: semantic models, deployment pipelines, workspace governance, RLS, and certified dataset standards.
  • Serve as the technical bridge between the Databricks Lakehouse and Power BI consumption layer; ensure models are reliable, performant, and self-service ready for business consumers.
  • Define and enforce BI engineering standards across the analytics team, covering DAX best practices, incremental refresh, composite models, and dataflow architecture.
• AI Platform Enablement & Data and AI Strategy
  • Evaluate, implement, and support AI and ML platform capabilities aligned with Waters' Data and AI strategy, including model lifecycle management, feature engineering, model registry, vector search, and AI gateway infrastructure on Databricks.
  • Ensure the end-to-end data estate is AI-ready: catalog completeness, data quality standards, lineage coverage, and access controls that support reliable model training, evaluation, and inference pipelines at scale.
  • Govern AI workloads on the platform: data access controls for training pipelines, model artifact storage, inference endpoint security, and audit trails that meet Waters' life-sciences compliance requirements.
  • Partner with data science, analytics, and business stakeholders to translate AI use case requirements into platform architecture decisions, building the infrastructure that enables AI outcomes without owning the models themselves.
  • Maintain current knowledge of AI platform capabilities across the stack (Databricks AI, Microsoft Copilot and Fabric AI, MLflow, and emerging open-source frameworks); provide evidence-based recommendations on adoption timing, cost, and risk.
• Platform Operations
  • Establish a Platform Operations discipline from the ground up: define runbooks, operational playbooks, change management standards, and escalation protocols for the full data estate.
  • Implement platform health monitoring and observability: dashboards, alerting, and incident response workflows that provide proactive visibility across the environment.
  • Own the on-call and incident management model for platform engineering: triage, root cause analysis, post-mortems, and continuous improvement loops.
  • Define and enforce a change management process for platform configuration, infrastructure updates, and governance policy changes across the direct and GCC teams.
• Direct Team & GCC Leadership
  • Lead and develop a direct team of platform engineers; conduct architecture reviews, set sprint priorities, and model disciplined engineering practices.
  • Own the delivery model for the matrixed GCC engineering team: define work packages, quality standards, SLAs, escalation paths, and onboarding protocols that make the GCC a genuine force multiplier.
  • Establish clear communication rhythms across time zones: async documentation standards, structured handoffs, and review gates that preserve quality without creating bottlenecks.
  • Grow individual engineers: define career paths, close skill gaps, and maintain team capability aligned to the platform roadmap.
• Data Governance, Security & Compliance
  • Own platform-level data governance: Unity Catalog permissions, data classification, lineage, and audit controls aligned with Waters' life-sciences compliance posture.
  • GxP and 21 CFR Part 11 awareness valued.
  • Enforce least-privilege access models, service principal governance, and cross-domain data sharing protocols.
  • Champion data quality, observability, and incident response practices; define SLAs and SLOs for platform reliability.
• Roadmap, FinOps & Stakeholder Partnership
  • Partner with the Senior Director to translate business priorities into platform roadmap milestones with clear ownership and delivery dates.
  • Own total cost of ownership for the data platform: Databricks compute governance, Power BI Premium capacity, FinOps discipline, and cloud spend accountability.
  • Engage Databricks, Microsoft (Azure/Power BI), and SAP vendor partners proactively to surface and leverage platform capabilities.
  • Represent platform engineering in architecture reviews, enterprise risk discussions, and IT steering committees.

Qualifications

• 10-plus years of hands-on experience in data platform engineering or data architecture; 5-plus years at a senior or lead level with direct team responsibility.
• Deep, current expertise in Databricks: Unity Catalog, workspace administration, compute governance, Delta Lake, and Lakehouse architecture.
• You can demonstrate this in a whiteboard or code review.
• Proven Power BI experience at enterprise scale: semantic models (tabular/DAX), deployment pipelines, workspace governance, and enterprise RLS.
• Strong Infrastructure as Code proficiency in Terraform for cloud data infrastructure, including CI/CD integration, state management, and module design best practices.
• Demonstrated experience leading both direct and GCC or distributed engineering teams; ability to build delivery models that create accountability across time zones.
• Solid foundation in cloud platforms (Azure or AWS), IAM, networking, and enterprise security patterns.
• Strong communicator, fluent in engineering depth and business context and credible with senior leadership, HR, and external candidates.
• Working knowledge of AI and ML platform patterns and MLOps: model lifecycle management, training pipeline infrastructure, model serving, and monitoring at enterprise scale.