Job Description

Senior Manager, Data Platforms -- $1B Manufacturing Business

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.

Key Responsibilities

Execution & Platform Operations

• 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.

Architecture, Design Patterns & Technology Choices

• 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
• 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 & Innovation

• 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.

Legacy Data Modernization

• Lead 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 Alignment

• 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.

Data Governance & Self-Service Enablement

• 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.

People Leadership & Talent

• 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.

Vendor & Partner Management

• Manage 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 Qualifications

• 10+ 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 Qualifications

• Manufacturing 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 Competencies

• Execution 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 Environment

• Fast-paced manufacturing business with multiple stakeholder groups and competing priorities.
• Hybrid collaboration with engineering teams, federated analytics users, and business partners