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.
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
