Sr. Manager, Data Platforms

Senior Manager, Data Platforms – $1B Manufacturing Business Role Summary

The 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.

• Own 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.

• Own 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, and 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.
• Enable 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.

• Lead modernization of legacy data ecosystems, including custom databases and SQL Server‑based solutions.
• Improve data lineage, auditability, and standardization while maintaining business continuity.

• Partner 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.

• Help 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.
• Manage 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.
• Manage vendor relationships across software and services.
• Own renewals, licensing considerations, services SOWs, partner performance, and cost/value tracking.

• Pre…