Sr. Manager, Data Platforms

Senior Manager, Data Platforms -- $1B Manufacturing BusinessRole SummaryThe Senior Manager, Data Platforms owns the execution and evolution of the data platform that powers analytics, AI/ML, and decision-making across a ~$1B manufacturing business. This leader combines strong delivery discipline with a builder mindset--modernizing legacy data assets (MSFT & SQL Server-based solutions) while enabling scalable, governed, self-service analytics for Supply Chain, Sales, Manufacturing and Finance use cases.

The role manages a small, highly technical team (3--4 data engineering / Foundry experts) plus contractors and partners, and serves as the owner for platform architecture, design patterns, and technology choices. The environment is fast-paced; success requires a self-starter who can align stakeholders and federated analytics communities around a shared platform strategy and operating model.

Key ResponsibilitiesExecution & Platform OperationsOwn day-to-day delivery and reliability of the Palantir Foundry data platform and its services (ingestion, transformation, orchestration, data quality, access controls, publishing/consumption). Establish and run a delivery operating rhythm: intake → prioritization → delivery → adoption → run support, with clear SLAs/SLOs and measurable KPIs. Manage platform backlog across multiple stakeholder groups; drive decisions, sequencing, and tradeoffs in a fast-moving environment. Provide oversight for contractors and systems integrators; ensure quality, documentation, and sustainable support models. Architecture, Design Patterns & Technology ChoicesOwn data platform architecture and standards across batch pipelines, data modeling, metadata management, governance, and consumption patterns. Define and enforce design patterns for: Source onboarding and change data capture (where applicable) Data quality checks and monitoring Reusable transformation frameworks Curated semantic/data products for analytics Secure publishing and entitlements Drive technology choices and platform evolution in environments such as Palantir Foundry and/or Snowflake / Databricks (or comparable modern stacks), ensuring fit-for-purpose, scalability, and cost discipline. AI/ML Enablement & InnovationEnable deployment of innovative ML models and analytics products into production--especially for high-value use cases such as Claims analytics (e.g., classification, root-cause clustering, anomaly detection, fraud/warranty signals, cycle time prediction). Identify opportunities to accelerate insights via automation, reusable components, and modern tooling; run pilots/POCs and scale what works. Legacy Data ModernizationLead modernization of legacy data ecosystems, including custom databases and SQL Server-based solutions: Improve data lineage, auditability, and standardization while maintaining business continuity. Business Partnership & Stakeholder AlignmentPartner with leaders across Supply Chain, Manufacturing, Sales, and Finance to define use cases, value metrics, and delivery roadmaps. Engage federated analytics users and embedded analysts to drive adoption of standardized datasets and self-service capabilities. Lead cross-functional governance forums to align definitions, ownership, prioritization, and data product SLAs. Data Governance & Self-Service EnablementHelp establish and continuously improve data governance: ownership, stewardship, cataloging, definitions, quality thresholds, and access policies. Champion self-service analytics (discoverable, trusted datasets; clear documentation; reusable metrics) while maintaining strong centralized governance and controls. People Leadership & TalentManage and develop a team of 3--4 technical experts (Palantir Foundry platform engineering); create growth paths and technical standards. Hire and attract engineering talent across experience levels--from early-career to senior specialists, building a balanced and scalable bench.

Build a high-accountability culture:

clear ownership, delivery commitments, and measurable outcomes. Vendor & Partner ManagementManage vendor relationships across software and services Own renewals, licensing considerations, services SOWs, partner performance, and cost/value tracking. Success Measures (First 12--18 Months)Predictable delivery and improved reliability of core data pipelines (reduced failures, faster recovery, better monitoring and alerting). Modernized legacy SQL Server/custom database dependencies with documented migration plans and measurable technical debt reduction. Standardized, curated data products adopted by Supply Chain, Manufacturing, Sales and Finance teams; improved trust in metrics and definitions. ML model deployment patterns established and at least 1--3 prioritized use cases operationalized (e.g., Claims). Establish governance (catalog coverage, defined ownership/stewardship, access controls, quality thresholds). Required Qualifications10+ years in data engineering, data platforms, or analytics engineering, including leadership experience. Strong hands-on experience with modern data platforms (e.g., Palantir Foundry, Snowflake, Databricks, or similar) and enterprise-scale data engineering. Demonstrated success building and operating batch pipelines, data models, and governed data products. Experience modernizing legacy data ecosystems (SQL Server-based solutions, custom databases, on-prem-to-cloud transitions). Proven ability to lead a small expert team and oversee contractors/partners to deliver production-quality outcomes. Strong stakeholder management skills--able to align federated analytics communities and senior leaders around shared priorities. Preferred QualificationsManufacturing domain experience (supply chain, production, quality, logistics) and understanding of operational data challenges. Experience enabling ML deployment/operationalization (feature pipelines, monitoring, model governance) in production environments. Familiarity with master data concepts, data catalogs, lineage, and governance tooling. Experience with cost management/FinOps for data platforms and usage-based licensing models. Working knowledge of APIs, event streams, and/or near-real-time patterns (where manufacturing use cases warrant it).

Core CompetenciesExecution excellence:

delivers predictable results; builds durable run/operate models.

Innovation mindset:

pilots new capabilities and scales proven solutions.

Self-starter:

thrives in ambiguity, creates clarity, drives momentum.

Architecture ownership:

defines patterns, guardrails, and platform direction.

Governed self-service:

expands access while maintaining strong controls and trust.

People leadership:

hires, develops, and retains high-performing technical talent. Working EnvironmentFast-paced manufacturing business with multiple stakeholder groups and competing priorities. Hybrid collaboration with engineering teams, federated analytics users, and business partners