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Versant Media, LLC is seeking a Sr. Technical Product Manager to lead analytics and measurement for Studio and Exhibitor products. This remote role emphasizes data-driven decision making, self-service analytics, and trusted metrics across Fandango datasets.
You will own the roadmap, define KPIs, build dashboards, and collaborate with data engineering, BI, and UX teams to deliver reusable analytics solutions. Prior experience with BI tools and AI-enabled workflows is required.
VERSANT (Nasdaq: VSNT) is an industry-changing media and entertainment business and home to trusted brands that shape culture, inform audiences, and build lasting connections. It operates across four core markets: political news and opinion, business news and personal finance, golf, and sports and genre entertainment. These markets are served through a powerful portfolio of iconic and innovative brands, including CNBC, MS NOW, USA Network, Golf Channel, Oxygen, E!, SYFY, and Versant's sports division USA Sports, along with complementary digital assets including Fandango, Rotten Tomatoes, GolfNow and GolfPass.
Fandango is seeking a Senior Technical Product Manager to build Studio and Exhibitor analytics and measurement products from trusted metrics, dashboards, and reports to chat and agentic experiences that help customers explore their data. This is a hands-on product role: you will define the roadmap and use SQL, BI tools, and AI agents to create and refine working analytics from curated Fandango datasets. Data Engineering prepares and governs those datasets; you own how they become useful measures, reports, and answers customers can trust.
This role shapes how data drives decision-making at Fandango. By enabling self-service analytics and improving data quality and accessibility, you empower teams to move faster, optimize performance, and unlock growth opportunities.
This is a remote role with opportunities for in-person collaboration (~10%) for team meetings, planning sessions, and key initiatives.
Own the roadmap, backlog, and delivery for Studio and Exhibitor analytics, measurement, dashboards, reports, and self-service experiences.
Work with Studio and Exhibitor users and customer-facing teams to define KPIs, calculations, comparisons, reporting needs, and acceptance criteria.
Shape chat and agentic analytics experiences: define the questions they should answer, the metric logic and data context they need, and the tools or workflows required to produce useful answers.
Execute studio-requested client-side tag placements, changes, removals, and validation on Adobe and Google analytics environments; support server-to-server CAPI measurement solutions with tag support.
Build and refine analytics work directly. Use SQL and AI-assisted or agentic workflows—including collaborating agents where useful—to create metric logic, queries, dashboard and report prototypes, and recurring analysis from approved datasets. Check calculations, source data, edge cases, and answer quality before release.
Prototype and configure dashboards and reports with AWS analytics and AI services, such as Amazon Quick Suite (including Quick Sight) and Amazon Bedrock AgentCore, and selected BI or chat tools such as Omni, Tableau, or ChatGPT.
Track accuracy, adoption, usability, documentation, and data access controls; work with BI, Data Science, Data Engineering, UX, and Engineering to productionize and improve the products.
6+ years in product management, BI, analytics, or data products, including analytics used by customers or business teams.
Strong SQL and data fluency: joins, grain, aggregations, metric definitions, reporting logic, and the ability to investigate unexpected results.
Hands-on experience building or configuring dashboards, reports, or analytics views in tools such as Amazon Quick Suite (including Quick Sight), Omni, Tableau, Looker, Superset, or similar.
Practical experience using generative AI and agentic workflows to produce analytics—not only to write requirements—including generating and checking queries, metrics, dashboards, reports, or natural-language data answers.
Ability to test AI-generated analysis for calculation accuracy, explainability, data permissions, and consistency with agreed metric definitions.
Understanding of event tracking, measurement tags or pixels, and conversion measurement; server-to-server APIs are a plus. Clear communication and prioritization across product, data, and engineering teams.