Technical Product Manager, Specialist

Core Responsibilities 

• Own technical product management for master data products, with end-to-end accountability across the data lifecycle: ingestion, transformation, modeling, orchestration, storage, and consumption. Actively contributes to solution design and continuous technical improvement. 

• Lead cross functional collaboration with data engineering, BI/analytics engineering, infrastructure, governance, and business teams to define and operationalize best practices for master data management (MDM), reference data management (RDM), data modeling, data quality frameworks, and data contracts. 

• Apply hands on SQL proficiency to explore datasets, validate requirements, assess data quality, and independently analyze issues and trends that inform prioritization, roadmap decisions, and stakeholder conversations.  

• Translate business needs into executable data product requirements, including hierarchies, schemas, transformations, data quality rules, SLAs, and downstream consumption patterns. 

• Coach and guide delivery teams on data product best practices—backlog structure, dependency management, technical tradeoff analysis, and outcome 

• based planning—while ensuring clear, consistent communication for data producers and consumers. 

• Partner with engineering and stakeholders to resolve production issues, including data quality defects, pipeline failures, orchestration errors, and downstream analytics breakages. Drive query level investigation, root cause analysis, and sustainable remediation. 

• Support the launch and adoption of master and reference data products by improving usability, trust, and consistency across operational systems, analytics, and reporting use cases. 

• Use quantitative analysis and data profiling to continuously improve data accuracy, completeness, consistency, and timeliness, balancing governance requirements with usability and speed. 

• Influence roadmap and prioritization decisions by representing data quality risk, architectural dependencies, and platform constraints in business planning discussions. 

• Participate in engineering delivery practices, including backlog refinement, sprint planning, design reviews, CI/CD for data pipelines, job orchestration controls, and observability for data quality and freshness. 

• Contribute to initiatives spanning metadata capture, data discoverability, governance workflows, and domain aligned ownership models, and support special projects as needed. 

Qualifications 

• Undergraduate degree or equivalent combination of training and experience required. Minimum of five years related work experience. Advanced degree in information technology or hands-on experience with data engineering, business intelligence / analytics engineering preferred. 

• Two years leading large cross-functional teams on major organizational projects preferred.