What are the responsibilities and job description for the Data Product Manager position at IT America Inc?
Position: Data Analytics with Product Management
Location: Austin, Texas – Hybrid (3D onsite, 2D Remote)
Duration: 12 Months
Job Description:
- At least 10 years of experience in product management, data platform delivery, analytics delivery, data engineering, enterprise technology delivery, or related roles.
- Experience managing product backlogs, roadmaps, epics, features, user stories, acceptance criteria, prioritization routines, and stakeholder engagement for data or technology platforms.
- Experience working with enterprise data platforms, cloud data ecosystems, lakehouse or warehouse environments, data pipelines, data quality, governance, metadata, lineage, semantic layers, BI platforms, or MDM capabilities.
- Experience with modern data and analytics technologies such as Databricks, Azure, Power BI, MicroStrategy, Snowflake, SQL, Python, Spark, orchestration tools, catalog or governance platforms, and data quality tooling preferred
- Experience supporting agile delivery teams in a scrum master, delivery lead, product owner, or product manager capacity.
Skills Required:
- Strong product management mind-set with ability to define value, prioritize demand, sequence delivery, manage trade-offs, and communicate roadmap decisions clearly.
- Working knowledge of SQL, data pipelines, ETL / ELT, analytics consumption patterns, dash boarding, data validation, platform observability, and cloud platform concepts.
- Strong understanding of modern data platforms, analytics platforms, semantic modeling, data quality, governance, MDM, metadata, lineage, access controls, and cloud data architecture concepts.
- Ability to translate complex technical needs into business-oriented product outcomes, delivery increments, acceptance criteria, and executive-ready updates.
- Strong agile delivery facilitation skills, including backlog refinement, sprint planning, dependency management, impediment removal, release coordination, and retrospective-driven improvement.