Data Architect

Data Architect

Reporting to the Chief Architect, Technology, and partnering closely with league, product, operations, marketing, ticketing, finance, and partnership teams across TMRW Sports' properties — TGL, WTGL, and the upcoming pro flag football league — the Data Architect will design, build, and govern the TMRW Lakehouse — the company's shared data foundation and central data library — that powers competition, broadcast, fan engagement, ticketing and match-night reporting, sponsorship analytics, and league operations from launch through future seasons.

TMRW Sports operates as a portfolio of leagues, each with its own ecosystem of vendor systems — competition and scoring platforms, broadcast and streaming partners, tracking and biometric providers, ticketing platforms, point-of-sale and F&B, finance, email marketing, sales leads, e-commerce, social and audience measurement, customer data, sponsorship measurement, and digital products. The Data Architect will consolidate these fragmented sources into the Lakehouse — a unified, governed platform; define repeatable integration patterns for onboarding new vendors and properties; and establish the foundation for AI/ML, AI data analyst agents, self-service analytics, and a single fan view that spans every TMRW Sports league.

This is a hands-on, builder-architect role. The Data Architect will set technical standards across data modeling, governance, security, and privacy; partner with vendors on API design, schema versioning, and SLAs; and remain in the modern data stack day to day, working with a lean in-house data engineering team and consulting partners and using AI to accelerate how quickly new sources are onboarded. This role requires occasional travel to venues during build phases and live competition windows.

Responsibilities

Data Platform Architecture & Delivery

• Own the architecture and day-to-day delivery of the TMRW Lakehouse — a governed foundation organized as a medallion architecture that serves analytics, ML, and operational use cases across the portfolio.
• Run the data-source onboarding roadmap with domain data owners — prioritizing which sources, definitions, and reports come into the Lakehouse first based on business value, and delivering against gated milestones where each phase proves value before the next begins.
• Define standardized integration patterns — batch, streaming, and change data capture — for onboarding new vendor systems, third-party feeds, and TMRW Sports properties.
• Design and maintain the canonical data model, semantic layer, and metric catalog that give every league a consistent, trusted view of fans, athletes, partners, and competition.
• Lead the modernization of legacy point-to-point integrations and manual, spreadsheet-driven reporting toward automated ELT pipelines and event-driven streaming where real-time matters — match-night reporting, game-day operations, venue IoT, broadcast, and digital products.
• Manage the economics of the data stack — consumption-based spend, third-party platform licenses, and consulting-partner capacity — and identify where in-house capabilities can reduce external agency and software costs.

Multi-Vendor Integration & the Single Fan View

• Consolidate fragmented data from competition systems, ticketing, customer data, POS, sponsorship measurement, broadcast/OTT, and digital products into a single source of truth.
• Build the identity resolution and fan intelligence foundation that powers marketing, ticketing, partnership activation, and league operations across TMRW Sports properties.
• Incorporate third-party data augmentation (e.g., demographic and household appends) with clear rules for how external attributes are validated, governed, and reconciled with first-party data.
• Evaluate, select, and onboard third-party data providers and feeds; partner with vendors on API design, schema versioning, data quality, and SLAs, and hold providers accountable to agreed data dictionaries and complete, timely delivery.
• Govern data contracts, data quality checks, and pipeline monitoring between producer systems and downstream consumers so the platform stays reliable and trusted as the league portfolio grows.

Governance, Security & AI/ML Enablement

• Establish and enforce data governance — cataloging, lineage, role-based access, PII handling, consent and preference management, GDPR/CCPA compliance — appropriate for a multi-league, multi-tenant environment.
• Operationalize TMRW's hub-and-spoke data operating model: help business leaders and maintain shared business definitions, KPI/metric calculations, and a searchable data catalog business users can find and trust.
• Enable AI/ML and agentic AI use cases — including in-house AI data analyst agents that let employees ask questions of governed data in plain language — by designing the semantic and agent-connectivity layers (e.g., MCP), feature store, vector/embedding infrastructure, and RAG/LLM patterns the leagues will rely on for personalization, content, and operations.
• Partner with security, legal, and privacy on data classification, masking, and access policy across leagues, partners, and rights holders.

Cross-Functional Partnership & Cross-TMRW Alignment

• Partner with the enterprise Analytics & Insights function and embedded league analysts to design and deliver the business intelligence platforms, semantic models, and governed datasets that enable trusted, self-service analytics across the organization — and drive adoption so dashboards replace manual, spreadsheet-based reporting.
• Raise data literacy across the organization through enablement, documentation, and common tooling, so business users can self-serve without deep technical skills.
• Translate business problems from ticketing, marketing, partnerships, competition, and broadcast teams into clean, well-owned data products.
• Drive alignment with cross-TMRW Sports architectures, leveraging common solutions across properties for scale and expediency.
• Communicate status, risks, tradeoffs, and decisions to executive leadership with clarity and urgency in a fast-paced, high-visibility environment — including the business value delivered (revenue enablement, cost avoidance, and time returned to teams) at each investment gate.

Requirements

• 10+ years of data engineering or data architecture experience, with 3+ years in a lead or architect role.
• Bachelor's degree in Computer Science, Data Science, Information Systems, Engineering, Mathematics, Statistics, or a related technical field.
• Hands-on production experience building on consumption based platforms, including medallion-style data organization, plus modern transformation and orchestration tooling.
• Strong dimensional modeling foundation; data vault familiarity a plus.
• Cloud fluency in AWS, including streaming and event-driven patterns.
• Demonstrated experience integrating fragmented data across multiple SaaS and vendor platforms and establishing repeatable onboarding patterns.
• Demonstrated experience standing up data governance with business data owners and stewards — shared definitions, metric catalogs, lineage, and PII/privacy controls.
• Experience leading delivery through a lean in-house team plus consulting partners — setting technical direction, reviewing partner work, and managing scope and cost.
• Experience designing semantic models and self-service BI that non-technical users adopt.

Preferred

• Experience in sports, sports media, sports betting/integrity, or live-event environments where reliability on a fixed schedule is critical.
• Hands-on experience enabling ML/AI workloads — feature stores, vector databases, RAG, agentic AI/LLM agents for analytics (including MCP-style tool integration), AI-assisted data engineering, and modern MLOps tooling.
• Experience with master data management, composable (warehouse-native) CDPs, reverse ETL, and third-party data enrichment.
• Experience in a startup or zero-to-one environment where the data platform is being built, not maintained.

You'll be successful if you:

• Make complex data concepts relatable to any audience — translating architecture into plain language instead of getting hung up on jargon.
• Are very hands on when needed, empowering when not, and know instinctively when to switch between the two.
• Are naturally optimistic and infect others with your optimism to collectively achieve great outcomes.
• Are collaborative with all stakeholders.
• Build a solid plan, often without all the necessary data, and lead a cross-discipline team to achieve the goal.
• Pivot from the plan when required to secure the overall outcome.
• Change your mind when new information is discovered.
• Inspire those around you to achieve greatness.
• Build team morale and maintain it even when the going gets tough.
• Treat data as a product — prioritize by