Senior Data Engineer

The Company

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. Jump was founded by e-commerce innovator Marc Lore, MLB legend Alex Rodriguez, and entrepreneur Jordy Leiser. We're backed by top investors including Alexis Ohanian's Seven Seven Six and Forerunner Ventures.

Our platform powers teams across the NBA, WNBA, and NWSL among other leagues, 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 is seeking a Senior Data Engineer to join our nimble Data team, where you'll help build and evolve the data platform that powers the company. You'll own the reliability and performance of our Snowflake and dbt-powered stack, building the pipelines and models that make data trustworthy and usable across Jump - from internal dashboards to client-facing products. As we create and iterate on agentic functionality across our product, you'll help shape how Jump builds on and leverages its data foundation. This is a hands-on, high-ownership role spanning data platform, analytics engineering, and data infrastructure. You'll work across the stack, solving problems end-to-end while helping shape the architecture and data practices that will support Jump as we scale.

What You'll Do

• Build and maintain ELT pipelines that turn raw data into modular, well-tested dbt models inside Snowflake

• Design and maintain reliable data orchestration and workflows as the platform grows in complexity

• Write and maintain dbt tests that catch issues before they reach a critical point

• Document models and sources so the meaning of our data is discoverable

• Build monitoring and alerting that surfaces pipeline failures and data anomalies before anyone has to ask

• Optimize Snowflake table design and materialization strategy to keep performance high and costs in check as data volume grows

• Help evolve Snowflake access controls and governance, keeping roles, grants, and security aligned with organizational standards

• Dig into what internal teams and external clients actually need from our data, not just what they ask for

• Bring a strong engineering discipline to data work; version control, CI/CD, code review

• Build the data infrastructure and governance needed to support new AI/ML use cases, including reliable access to high-quality, well-understood data

What We're Looking For

• 6+ years of data engineering experience

• Ability to independently own complex data systems from design through production and make sound architectural tradeoffs

• Strong product and stakeholder instincts - you can translate ambiguous business needs into durable data solutions

• Deep, production-grade SQL and Snowflake expertise

• Deep experience with dbt, including testing, documentation, dependencies, and deployment

• Software engineering discipline — Git-based version control, CI/CD for data pipelines, and strong Python skills for building data pipelines, orchestration, and custom transformations

• A track record of building testing, monitoring, and alerting into pipelines before something breaks, not after

Nice to Haves

• Familiarity with BI/analytics tools, ideally Sigma

• Experience with orchestration and workflow tooling

• Depth with cloud platforms

• Exposure to supporting ML/AI pipelines or streaming and real-time data