Product Owner - MTS Data
Bridgestone · Automotive
- Bengaluru, India
- On-site
- Posted today
- Product Management
About the job
Experience: 6+
Job Description: Product Owner – Data Platform
Location: Bangalore, India Company: Bridgestone Employment Type: Full-Time
Role Overview
Bridgestone is seeking an experienced Product Owner – Data Platform to drive the strategy, development, and continuous enhancement of data products and platform capabilities that support business decision-making and operational efficiency.
The Product Owner will act as the bridge between business stakeholders and technical teams, translating business needs into clear product requirements, prioritized backlogs, and measurable outcomes. The role will work closely with Data Engineers, Data Analysts, Architects, and other cross-functional teams to deliver reliable, scalable, high-quality data solutions that generate business value.
The ideal candidate combines strong product ownership and stakeholder management skills with a solid understanding of modern data platforms, data engineering, analytics, and cloud technologies. The candidate should be comfortable translating business requirements into data use cases, analytical requirements, data models, dashboards, and actionable technical stories.
Key Responsibilities
A. Product Strategy and Roadmap
Define and maintain the vision, strategy, and roadmap for data platform products in alignment with Bridgestone's business priorities.
Identify opportunities to improve business processes, reporting capabilities, data accessibility, and operational efficiency through data-driven solutions.
Translate business objectives into product goals, measurable outcomes, and prioritized initiatives.
Evaluate and prioritize feature enhancements, platform capabilities, data integration requirements, and technical improvements based on business value, customer impact, risk, and effort.
Drive continuous improvement and innovation across data products and platform capabilities.
B. Business Requirement Analysis and Product Backlog Management
Engage with business stakeholders to understand business processes, challenges, reporting needs, KPIs, and data requirements.
Translate business requirements into epics, features, user stories, use cases, functional specifications, and clear acceptance criteria.
Own and prioritize the product backlog, ensuring requirements are refined and ready for development.
Collaborate with Data Analysts to define business metrics, KPIs, reporting requirements, dashboard expectations, and analytical use cases.
Work with Data Engineers to translate business requirements into data ingestion, transformation, integration, modeling, and pipeline requirements.
Resolve requirement ambiguities, manage dependencies, and ensure alignment between business expectations and technical implementation.
C. Data Platform and Product Delivery
Partner with Data Engineering teams to deliver scalable and reliable data pipelines, data transformations, and platform capabilities.
Understand the principles of modern data architectures, including Medallion Architecture (Raw, Silver, and Gold layers), data ingestion, transformation, and data modeling.
Facilitate discussions on source-to-target mapping, data availability, refresh frequency, dependencies, data lineage, and downstream consumption.
Coordinate delivery of data products built using AWS, Databricks, Spark/PySpark, SQL, and related technologies.
Work with engineering teams on data integration requirements involving AWS services such as S3, Glue, Aurora/RDS, Lambda, and Step Functions.
Participate in sprint planning, backlog refinement, estimation, sprint reviews, release planning, and dependency management.
Proactively identify and resolve delivery blockers, scope conflicts, and cross-team dependencies.
D. Collaboration with Data Engineers and Data Analysts
Serve as the primary point of contact between business stakeholders, Data Engineers, and Data Analysts.
Ensure Data Engineers have clear requirements for pipeline development, transformations, data models, validation, reconciliation, and operational workflows.
Ensure Data Analysts have a clear understanding of business questions, KPI definitions, data requirements, dashboard specifications, and expected insights.
Coordinate the complete flow from business requirement discovery through data ingestion, processing, validation, analytics, and business consumption.
Facilitate collaboration between engineering and analytics teams to resolve data discrepancies, quality issues, and inconsistencies in business metrics.
Ensure that delivered data products are fit for purpose, documented, validated, and ready for business adoption.
E. Data Quality, Governance, and Reliability
Define and prioritize data quality expectations covering accuracy, completeness, consistency, timeliness, and reliability.
Partner with Data Engineers and Data Analysts to establish appropriate validation rules, reconciliation processes, quality checks, and monitoring requirements.
Ensure data defects are prioritized based on business impact and that root causes and corrective actions are tracked to closure.
Collaborate with Architecture, Security, and relevant governance teams on data access, privacy, security, and compliance requirements.
Ensure data products meet agreed non-functional requirements, including availability, performance, scalability, and operational support.
Drive improvements in data reliability and reduce recurring data quality issues and production incidents.
F. Stakeholder Management and Communication
Act as the primary liaison between business stakeholders, product teams, engineering teams, and analytics teams.
Communicate product roadmaps, priorities, delivery progress, risks, dependencies, and release plans transparently.
Facilitate requirement workshops, stakeholder discussions, backlog prioritization sessions, and product demonstrations.
Manage competing priorities and expectations across multiple stakeholders while maintaining focus on business outcomes.
Present complex data and technical concepts in clear business terms to support informed decision-making.
Build strong working relationships with business teams and ensure stakeholder feedback is incorporated into product planning.
G. Product Performance and Business Value
Define and monitor KPIs to measure product adoption, business impact, data quality, platform reliability, and delivery effectiveness.
Evaluate the business value of data initiatives and prioritize work based on measurable outcomes.
Gather stakeholder feedback and assess whether delivered data products meet business expectations.
Conduct post-release reviews to identify improvement opportunities and incorporate lessons learned into the roadmap.
Identify opportunities to improve reporting efficiency, reduce manual data preparation, accelerate access to insights, and improve operational decision-making.
Track benefits realization and communicate measurable outcomes to business stakeholders.
Required Qualifications
Bachelor's degree in Computer Science, Engineering, Information Systems, Business, or a related field.
5+ years of experience in Product Ownership, Product Management, Business Analysis, or a related role.
Experience working with data platforms, data products, business intelligence, analytics, or data engineering teams.
Demonstrated experience gathering business requirements and translating them into epics, user stories, functional specifications, and acceptance criteria.
Strong stakeholder management, communication, requirement analysis, prioritization, and problem-solving skills.
Experience working with cross-functional teams comprising Data Engineers, Data Analysts, Architects, QA, and business stakeholders.
Strong understanding of Agile/Scrum methodologies, backlog management, sprint planning, estimation, and iterative product delivery.
Working knowledge of SQL and relational database concepts, with the ability to understand data structures, transformations, and data validation requirements.
Understanding of data pipelines, ETL/ELT processes, data warehousing, data modeling, data quality, and reporting/analytics workflows.
Familiarity with product management tools such as Jira and Confluence.
Ability to balance business priorities, technical dependencies, platform reliability, and long-term product scalability.
Preferred Qualifications
Experience owning data platform products or enterprise data and analytics initiatives in a large organization.
Familiarity with AWS data services, including S3, Glue, Aurora/RDS, Lambda, and Step Functions.
Understanding of Databricks, Spark, PySpark, SQL, Python, and Medallion Architecture.
Exposure to data visualization and BI tools such as Power BI, including dashboards, KPI reporting, and business metrics.
Understanding of data ingestion, transformation, source-to-target mapping, data reconciliation, and pipeline orchestration.
Experience with data modernization, platform migration, integration, and enterprise-scale data initiatives.
Familiarity with API-based integrations and third-party data sources.
Experience defining data quality rules, governance requirements, data access controls, and operational monitoring expectations.
Experience working with distributed teams and stakeholders across different geographies and time zones.
Relevant product management, Agile, cloud, or data-related certifications are an advantage