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The Team ConvergeSPORTS is a product-driven business combining Deloitte's sports industry experience with proprietary data, AI-native decision intelligence, and reusable Converge capabilities. We help sports organizations grow revenue, deepen fan engagement, optimize partnerships, and improve commercial performance while operating with the focus of a product company. The Analytics & Insights product team builds reusable data, modeling, and decision capabilities for sports organizations. The team works across Product Management, Data Engineering, Data Science, Software Engineering, Forward Deployed Engineering, and Go-to-Market to turn complex sports and consumer data into production-ready product capabilities. Within Analytics & Insights, this role leads the data products and engineering capabilities that turn sports, fan, sponsorship, engagement, and commercial data into governed, reusable services, models, APIs, and activation-ready interfaces. The team builds product-grade foundations for analytics, decision workflows, and AI-enabled experiences rather than one-off client implementations. Position Summary ConvergeSPORTS is seeking a Head Product Manager specializing in data and product engineering to lead the Analytics & Insights product strategy and delivery model. You will own the vision, architecture-aligned roadmap, operating model, and cross-functional execution required to turn fragmented data into trusted, scalable product capabilities. This is a hands-on functional leadership role for a technically fluent product leader who can guide data and software engineering priorities, establish product standards, and connect foundational investments to adoption, reliability, and commercial outcomes. Recruiting for this role ends on 09/22/2026. Work you'll do As the product leader for data and product engineering within Analytics & Insights, you will set the strategy and operating rhythm for data products, shared services, and reusable engineering capabilities while working day to day with Data Engineering, Product Engineering, Data Science, Design, Forward Deployed Engineering, and Go-to-Market leaders. Your work will include: Define and own the multi-year product vision, data product strategy, roadmap, and outcome metrics for shared data and product engineering capabilities. Establish the product model across data ingestion, identity resolution, audience profiles, semantic models, data products, APIs, activation services, and developer-facing capabilities. Translate market and product needs into prioritized product epics, data contracts, interface specifications, nonfunctional requirements, acceptance criteria, and release plans. Partner with Data Engineering and Product Engineering leaders to shape reference architecture, reusable services, integration patterns, cloud platform choices, technical-debt priorities, and build-versus-buy decisions. Set product requirements and standards for data quality, lineage, metadata, observability, privacy, consent, access controls, testing, reliability, and production support. Lead roadmap and backlog decisions for Analytics & Insights capabilities consumed by product teams, Data Science, Forward Deployed Engineering, and Go-to-Market teams. Define AI-native and agentic product capabilities, including governed data access, retrieval and tool interfaces, evaluation criteria, human review points, and guardrails. Build and lead the product operating cadence across discovery, architecture reviews, backlog grooming, sprint planning, demonstrations, release readiness, adoption reviews, and incident learning. Create implementation, configuration, and onboarding patterns that reduce the time required to integrate new clients and datasets while protecting reusable architecture and product scalability. Own adoption, commercialization, and value measures, including data quality, integration time, service reliability, reuse, feature adoption, cost to serve, client impact, and commercial contribution. The successful candidate would possess these skills: Technical product leadership that connects data architecture, data engineering, software engineering, and end-user value. Systems thinking across data domains, APIs, services, product workflows, security, reliability, and operating constraints. Ability to make clear portfolio and roadmap tradeoffs across foundational data and engineering work, customer needs, technical debt, and commercial priorities. Executive-ready communication and influence across Product, Engineering, Data Science, Delivery, Sales, and account leadership. Team-building and coaching skills that create accountability, decision clarity, and high-quality product execution across distributed teams. Hands-on ownership style with a willingness to write requirements, inspect data models and APIs, review prototypes, interrogate metrics, and support demonstrations. Qualifications Required: Bachelor's degree in Business, Engineering, Computer Science, Data Science, Information Systems, or a related field, or equivalent professional experience. 8+ years of experience in product management, technical product management, data product management, product engineering, or software platform delivery. 5+ years of experience owning product strategy, roadmaps, backlogs, release decisions, and success measures for data products, SaaS platforms, developer products, or analytics products. 4+ years of experience partnering directly with data engineering and software engineering teams to define data models, APIs, pipelines, platform services, cloud capabilities, or production requirements. 3+ years of experience with at least two of the following: customer data products, identity resolution, CRM or loyalty data, cloud data platforms, event or batch pipelines, data governance, API products, or ML/AI product features. Experience taking at least one data product, developer service, or shared engineering capability from discovery and architecture through production release, adoption measurement, and ongoing operating support. Experience leading at least two concurrent cross-functional workstreams across product, data engineering, application engineering, data science, design, or client delivery using Agile delivery practices and tools. Ability to travel up to 25%, on average, based on client, market, and product needs. Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future. Preferred: 2+ years of experience in sports, media, entertainment, sponsorship, fan engagement, loyalty, ticketing, or adjacent consumer industries. 2+ years of experience with customer data products, identity resolution, audience segmentation, CRM, loyalty, ticketing, or activation products. Experience with one or more cloud data or product technologies such as Snowflake, Databricks, AWS, Azure, GCP, Kafka, dbt, REST APIs, or GraphQL. Experience defining data governance, consent and privacy, data quality, observability, service-level objectives, or platform reliability requirements in a production environment. Experience defining or launching GenAI or agentic product features using governed enterprise data, including grounding, tool use, evaluation, or human-in-the-loop controls. The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonab
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.