Scientific Games:

Scientific Games is the global leader in lottery games, sports betting and technology, and the partner of choice for government lotteries. From cutting‑edge backend systems to exciting entertainment experiences and trailblazing retail and digital solutions, we elevate play every day. We push game designs to the next level and are pioneers in data analytics and iLottery. Built on a foundation of trusted partnerships, Scientific Games combines relentless innovation, legendary performance, and unwavering security to responsibly propel the global lottery industry ever forward. Position Summary

Job Summary: Working‑lead role that owns product definition quality for the highest‑complexity opportunities in the Data Products portfolio - output quality across the full lifecycle, from problem framing through Ready‑to‑Build handoff. Insight is the starting focus. This role sets the standard for the rest of the team by demonstrating it directly, not by directing others' work. Scope

Owns product definition quality for the highest‑complexity opportunities in the Data Products function, working as an individual contributor. Partners with Platform Engineering, Data Science/ML, and business stakeholders. Sets the standard Solutions Analysts follow - without formal authority over their day‑to‑day work or performance. Reports to the Director, Data Products. Essential Job Functions

Own output quality across the full product definition lifecycle, from problem framing through Ready‑to‑Build handoff,



on the highest‑complexity opportunities Set the standard for what a well‑defined opportunity looks like - and demonstrate it directly in your own work Make the call on what moves forward Build repeatable methods and templates that Solutions Analysts can execute without your involvement in every decision Author product definitions, business metric tables, and agent context specs for the highest‑complexity opportunities Lead deployment channel decisions - Databricks‑native vs. Teams, Dynamics, or another end‑user tool Maintain the business context layer: metric definitions, metadata, and decision rules that keep AI products reasoning correctly Represent product definition in cross‑functional reviews with Platform Engineering and DS/ML Raise the judgment of Solutions Analysts through direct feedback, documented standards, and context - without a reporting relationship Ensure handoffs to Platform Engineering are clean, and define for DS/ML what models must produce and within what constraints Qualifications

Required: Bachelor's degree in a relevant field 7+ years in data, analytics, or product, with deep hands‑on ownership of product definition work end‑to‑end (people‑management experience not required) Product definition depth - can write a well‑structured product spec, business metric table, and agent context document, and knows the difference between a complete one and one that causes problems downstream Technical fluency without engineering scope - understands what Databricks, Teams, and Dynamics can do well enough to make deployment decisions, without reaching into Platform Engineering's territory Working‑lead instinct - most effective close to the work,



and sets standards by demonstrating them rather than directing others Influence without authority - can shape how Solutions Analysts and peers approach their work through standards, documentation, and direct coaching, with no reporting relationship Operating model discipline - manages the boundary with Platform Engineering and DS/ML cleanly, and holds that line under pressure Desired: Hands‑on experience defining AI/agentic products (semantic‑layer or metadata‑driven products) Familiarity with Databricks, Microsoft Teams, and Dynamics as delivery channels Authority To:

Set and enforce the Ready‑to‑Build standard for product definitions Decide what product definition work moves forward to handoff Make deployment channel decisions (Databricks‑native vs. Teams, Dynamics, or other) Requires Approval For:

Roadmap changes and current commitments (owned by the Director) Cross‑functional escalations that change Platform Engineering or DS/ML scope Prioritizing Discovery Analysts' work (owned by their manager - this role influences, not directs) Key Contacts:

Internal

Director, Data Products Platform Engineering Data Science/ML Business stakeholders Discovery Team

External

Technology and platform vendors (e.g., Databricks) Lottery customers

Physical Requirements

The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. While performing the duties of this job, the employee is regularly required to sit, stand, walk, bend, use hands, operate a computer, and have specific vision abilities to include close and distance vision, and abilit

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📌 Senior Data Product Owner (Winnipeg)
🏢 Sglottery
📍 Winnipeg