Data Product Manager
About The Position
MNIT/DCYF is seeking a Data Product Manager to lead the strategy, roadmap, and execution of data initiatives that transform organizational data into scalable, high-value data products, including curated datasets, analytics platforms, and data infrastructure. The role will work closely with data engineering, data science, analytics, enterprise architecture, and business teams to modernize legacy mainframe data environments and transition toward data lakes and modern data curation platforms. The Data Product Manager will guide products through the full lifecycle, from ideation and design through development, deployment, monitoring, and deprecation.
Requirements
- 4–7 years of experience in data management, data analytics, data engineering, or related fields.
- Strong Product Management / Product Leadership experience.
- Strong understanding of data systems, data pipelines, data warehousing, data modeling, metadata, and data governance.
- Experience collaborating with Data Architecture, Data Engineering, and Data Science teams.
- Ability to translate complex technical concepts into business-friendly language.
- Strong communication, prioritization, stakeholder management, and analytical skills.
- Experience with analytics and BI tools such as Power BI, Tableau, Looker, dbt, or Google Analytics.
- Experience with SQL, data lakes, data pipelines, and ETL.
- Significant experience with Databricks.
- Familiarity with Java and Python.
- Experience implementing modern data architectures.
- Experience building internal platforms or developer-facing data products.
- Understanding of enterprise data sharing constraints and data-sharing agreements.
- Experience in a highly regulated environment involving statistical analysis and reporting.
Responsibilities
- Define the vision, strategy, and roadmap for enterprise data products.
- Identify high-value opportunities based on business needs, data landscapes, and organizational priorities.
- Lead the end-to-end data product lifecycle including requirements, design, development, testing, launch, and iteration.
- Partner with data engineers and data scientists to build scalable pipelines, models, and data services.
- Ensure data quality, governance, lineage, metadata, and documentation standards.
- Translate business logic into data transformations, metadata, and domain-specific rules.
- Serve as the primary liaison between technical teams and DCYF business stakeholders.
- Define success metrics, KPIs, dashboards, and reporting frameworks.
- Ensure data products provide actionable insights and support data-driven decision making.
- Promote responsible data use, privacy, governance, compliance, and ethical AI practices.
- Support modernization from legacy mainframe systems to modern data lakes and data platforms.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
