Domain Data Product Owner

Position Overview

The Manufacturing Data Lead (Domain Product Owner) serves as the primary on-site data and analytics leader at the manufacturing plant. This role acts as the bridge between shop-floor (Gemba) operational challenges and enterprise-level data. The position is responsible for ensuring that manufacturing data initiatives are driven by real business value, aligned with plant priorities, and executed efficiently without creating digital waste. The role requires strong hands-on experience in data analysis, manufacturing systems, and close collaboration with plant stakeholders and enterprise data teams.\

Key Responsibilities

1. Manufacturing Data Strategy & Gemba Engagement

Conduct regular on-site Gemba activities ( Genchi Genbutsu ) to validate that manufacturing data in MOM/MES and related systems accurately reflects actual shop-floor conditions.

Identify, assess, and document local manufacturing data sources (e.g., traceability systems, maintenance data, PLC logs) and data integrity gaps before escalation to enterprise platforms.

Translate plant-specific KPIs (e.g., OEE, Yield, Downtime, Root Cause Analysis) into a structured, multi-year manufacturing data roadmap aligned with global data strategies.

1. Technical Product Ownership & Requirements Management

Act as the single point of contact for all manufacturing data initiatives at the plant.

Convert ambiguous manufacturing pain points into well-defined, high-quality JIRA epics and user stories.

Define clear acceptance criteria for data products from the perspective of operators, engineers, and plant leadership.

Review, filter, and validate enterprise data team proposals to ensure sufficient domain context before on-site execution.

1. Demand Intake, Prioritization & Resource Optimization

Manage and prioritize the intake funnel for all plant data requests based on business impact and ROI.

Serve as the final decision-maker on which initiatives are promoted to the enterprise backlog.

Ensure efficient use of enterprise data resources by preventing unstructured or exploratory activities that do not deliver validated value to the plant.

1. Cross-functional Team Collaboration:

Partner with internal business teams including production engineering team and IT groups at the plant site to understand requirements

Participate in cross-functional meetings to align with system architecture and performance goals.

Prioritize against engaged priorities and collaborate across IT teams and internal business teams to deliver on business objectives.

1. Documentation and Reporting:

Maintain accurate documentation of business requirements and reports form meetings

Requirements

Qualifications and Skills:

Demonstrate practical experience and knowledge of plant floor operations.

Strong understanding of database architecture, management, and security principles.

Proficiency in PostgreSQL, including advanced query optimization techniques.

Excellent analytical and problem-solving abilities.

Strong communication and collaboration skills.

Technical & Professional Experience

Minimum 5 years of experience in data analysis, data engineering, or systems integration.

Strong hands-on experience with SQL and understanding of PLC / IoT data structures.

Proven experience working in a high-volume manufacturing environment (Toyota manufacturing experience preferred).

Solid understanding of manufacturing concepts such as cycle time, takt time, downtime, and production KPIs.

Advanced proficiency with JIRA, Confluence, and Agile / product management methodologies.

Core Competencies & Mindset

Problem Solving: Strong root-cause analysis mindset; focuses on solving underlying data and process issues rather than surface-level symptoms.

Communication: Ability to clearly explain complex data and system concepts to plant leadership, and manufacturing constraints to technical and cloud engineering teams.

Collaboration: Works effectively across plant operations, IT, and enterprise data teams to deliver measurable outcomes.

Ownership: Demonstrates accountability for data quality, prioritization decisions, and delivered business value.

Performance & Success Indicators

Reduction in lead time from problem identification to JIRA-ready requirements.

Improved data integrity between shop-floor reality and enterprise dashboards.

Measurable improvements in manufacturing KPIs (e.g., OEE, downtime reduction) driven by delivered data solutions.