Jump
Technology
DataPlatformLead
Neural analysis suggests this role is
optimal for Lead candidates.
“Data Platform Lead at Jump. Skills: Data platform, AI on data, Data engineering team, Cloud data warehouse. Own data platform strategy. Define ingestion architecture”
Industry & Context.
Troubleshooting; Root cause analysis
What They're Looking For.
Must Have
8+ years in data engineering, 2+ years in leadership role, Managing and developing engineers, Hands-on experience with cloud data warehouse, Hands-on experience with transformation stack, Proven experience building AI on structured data, Deep expertise in data ingestion at scale, Experience building productized data offering, Solid engineering fundamentals, Version control, Code review, CI/CD, Infra-as-code
Nice to Have
Direct Snowflake + dbt experience, Advanced in-warehouse AI capabilities, Agent-accessible data patterns, ML or MLOps experience, AWS fluency, Experience with modern BI tooling, Experience with self-serve analytics delivery, Sports industry experience, Live events industry experience, Time-series experience, EU data residency experience, GDPR experience, Multi-region warehouse patterns experience
What You'll Do.
Own data platform strategy
Define ingestion architecture
Build scalable infrastructure
Manage transformation pipelines
Develop modern BI layer
Grow platform with clients
Grow platform with data sources
Drive AI-on-data layer
Define agent/LLM data access
Define agent/LLM data query
Define agent/LLM data action
Build productized integrations
Ingest data from sources
Productize integrations motion
Lead data engineering team
Develop data engineering team
Manage data engineers
Set technical direction
Define engineering standards
Implement infra-as-code
Implement data lineage
Implement observability
Ensure platform scales
Be hands-on on warehouse
Be hands-on on transformation
Contribute to Snowflake
Partner with go-to-market
Define client onboarding
How You'll Work.
Team & Collaboration
Partner with AI engineer; Partner with Product; Partner with Engineering; Partner with go-to-market; Work across teams
Communication Scope
Clear communicator; Direct feedback
Process & Methodology
Agile
Full Job Description
Description Jump is transforming the live sports experience with the only end-to-end fan engagement platform built specifically for sports teams and venues. By focusing on aligned incentives between teams and fans, our platform unifies ticketing, merchandise, and game day operations - creating a smoother, more fan-friendly experience. Founded in 2021 by e-commerce innovator Marc Lore, MLB legend Alex Rodriguez, and entrepreneur Jordy Leiser, we’ve raised $60 million from top investors including Alexis Ohanian’s Seven Seven Six and Forerunner Ventures. Our platform powers teams across the NBA, WNBA, and NWSL helping them boost ticket sales and deliver innovative fan experiences. We’re a remote-first team driven by core values - begin and build with trust, play like the underdog, win as a team, and do your thing. If you’re collaborative, adaptable, and eager to shape the future of live sports, Jump is the place for you. The Role Jump's data platform is at an inflection point. What started as an internal capability now powers the analytics and insights our clients rely on every day. We're turning it into a full product: scalable, multi-tenant data infrastructure, an expanding library of integrations pulling from a growing set of first- and third-party data sources, and an AI intelligence layer that transforms raw data into actionable decisions. As Data Platform Lead, you'll own the vision and execution of that platform. You'll lead and grow our data engineering team, partner closely with our AI engineer to shape how agents and LLMs consume and act on our data, and work across Product, Engineering, and go-to-market teams to build something that scales — not a series of one-off implementations. The platform you build here will become core infrastructure for how our clients run their business. What You'll Do - Own the data platform strategy end-to-end — from ingestion architecture and scalable, multi-tenant data infrastructure to transformation pipelines, a modern BI laye
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