OverviewThe Director, Data Platform Engineering owns this platform end to end: the infrastructure, the governance model, the engineering standards, and the architecture that connects every system in our data estate to every team that depends on it. Data engineering, analytics, data science, commercial, finance, and operations teams across 100-plus countries rely on what you build. This is a hands-on role. You will architect, build, and govern alongside your team, not above them, while leading a direct platform engineering team and a matrixed Global Capability Center (GCC) team with a delivery model that keeps both aligned and the platform moving forward. If you want a role where the platform you build has company-wide reach and a direct line to Waters' Data and AI strategy, this is it.
You will report to the Senior Director, Enterprise IT Data and Analytics and serve as the technical authority for Waters' Enterprise Data Platform, accountable for platform reliability, governance, cost efficiency, and engineering excellence across a global, distributed team.
ResponsibilitiesKey ResponsibilitiesHands-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. You 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. You are their platform partner, not their ticket queue.
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: Establishing the Practice- 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.
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.
QualificationsRequired 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.
Preferred Qualifications- 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.
