Job Description & How to Apply Below
We’re looking for a Technical Product Manager to work on the KORE Intelligence platform. You'll be joining the Partnership Measurement team, responsible for evolving our data pipeline – starting with our Sport
GraphTM through to ingestion, enrichment, analysis and visualization.
You'll take ownership of backend‑heavy capabilities to source and enrich the right data to help our clients understand the value & impact of their partnerships and ultimately to optimize those partnerships to drive greater impact. This includes managing data ingestion frameworks, segmentation infrastructure, and the tooling used by internal teams to deliver commercial outcomes 'll work closely with external vendors, internal engineering and data teams, and cross‑functional stakeholders to build secure, scalable, and highly performant systems that support the measurement, reporting & optimization of sports & entertainment partnerships.
This role is ideal for a technical product manager who thrives on solving backend data challenges, enjoys working at the intersection of engineering and platform architecture, and understands the role data can play in enabling downstream value. You’ll collaborate closely with data engineers, scientists, analysts, and commercial leads to develop solutions that scale, solving real‑world problems for teams, leagues, rights‑holders and brands.
WHAT YOU’LL BE DOING
Own the product lifecycle for 1-2 product squads working on the Partnership Intelligence platform, focusing on sourcing, scalability, data quality, and downstream value enablement (e.g. segmentation, reporting)
Work with internal and external stakeholders to identify key use cases, pain points, and automation opportunities across data workflows
Shape the roadmap for core data capabilities, balancing data availability, data quality, and engineering complexity to drive commercial value
Evolve Sport Graph, our proprietary digital twin of the Sport & Entertainment ecosystem – focused on maintaining quality while expanding its reach with new and enriched data
Expand our Omni‑channel footprint to every touchpoint relevant to measuring and optimizing the impact of a partnership
Improve our estimation, valuation and contextualization models
Utilize AI in ways that help us move faster, smarter and more efficiently, while unlocking new value for our business and our clients
Work with external partners (e.g. X, Meta, You Tube) to understand API specifications, access protocols, and integration constraints
Write clear functional requirements, user stories, and technical acceptance criteria; contribute to backlog prioritization and sprint planning
Lead and contribute to sprint ceremonies (e.g. refinement sessions, stand ups)
Ensure data governance, compliance (e.g. GDPR/CCPA), and observability are baked into all products
Ensure data quality & accuracy via development of monitoring systems
Contribute to documentation, onboarding guides, and support workflows
Delivered improvements to our ingestion and enrichment systems that enhanced the accuracy, quality and usefulness of our data
Helped roll out a partnership scoring capability that became a core input into client marketing and reporting use cases
Led the integration of 3 new data sources that gave our clients a view into partnership performance that was previously a blind spot
Vastly increased the number of nodes and connections within Sport Graph
REQUIREMENTS
3–5 years in product management, with a focus on data platform, products, or internal tools; engineering (especially data engineering) background a bonus
Strong understanding of data lifecycle management: ingestion, transformation, modelling, governance, and analytics enablement
Experience working with modern data infrastructure and tooling (e.g. Airflow, Azure Data Factory, Databricks, Snowflake, DBT, Redshift)
Able to navigate technical discussions with engineers and translate them into clear requirements for delivery
Comfortable in agile, cross‑functional environments; proactive communicator and problem solver
Familiar with data quality and monitoring practices, data observability, schema standardization, and documentation workflows
Familiar…
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