Director, Data Platform Engineering

• Build and formalize a Platform Operations discipline from the ground up: define runbooks, operational playbooks, change management standards, and escalation protocols for the full data estate.
• Establish SLAs and SLOs for platform reliability: Databricks workspace uptime, job success rates, Power BI refresh SLAs, and data pipeline latency targets.
• 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.

• 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.

• 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.

• 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.

• 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.

• Experience with SAP BPC (Business Planning & Consolidation) or SAP SAC (Analytics Cloud) in an enterprise environment.
• Background in a regulated industry such as life sciences, pharma, or medical devices, with familiarity with GxP or 21 CFR Part 11 data integrity requirements.
• Databricks certifications (Data Engineer Professional, Platform Administrator, or Architect).
• Experience with GitHub Actions CI/CD pipelines for data infrastructure.
• FinOps experience: cost tagging, cluster right-sizing, compute policy design, and cloud spend forecasting.
• Hands-on experience with GenAI or LLM infrastructure: RAG architectures, vector databases, embedding pipelines, or AI and LLM gateway configuration on an enterprise data platform.

Waters Corporation (NYSE:WAT) is a global leader in life sciences and diagnostics, dedicated to accelerating the benefits of pioneering science through analytical technologies, informatics, and service.

With a focus on regulated, high-volume testing environments, our innovative portfolio harnesses deep scientific expertise across chemistry, physics, and biology.

We collaborate with customers around the world to advance the release of effective, high-quality medicines, ensure the safety of food and water, and drive better patient outcomes by detecting diseases earlier, managing routine infections, and combating antibiotic resistance.

Through a shared culture of relentless innovation, our passionate team of ~16,000 colleagues turn scientific challenges into breakthroughs that improve lives worldwide.

Diversity and inclusion are fundamental to our core values at Waters Corporation. It benefits our employees, our products, our customers and our community. Waters complies with all applicable federal, state, and local laws. Qualified applicants are considered without regard to sex, race, color, ancestry, national origin, citizenship status, religion, age, marital status (including civil unions), military service, veteran status, pregnancy (including childbirth and related medical conditions), genetic information, sexual orientation, gender identity, legally recognized disability, domestic violence victim status, or any other characteristic protected by law. Waters is proud to be an equal opportunity workplace and is an affirmative action employer. All hiring decisions are based solely on qualifications, merit, and business needs at the time.