Data Platform Lead

Jump

Data Platform Lead driving the data platform strategy for sports engagement at Jump. Leading the data engineering team to create scalable, multi-tenant data infrastructure and AI capabilities.

Posted 6/4/2026full-timeRemote • 🇺🇸 United StatesSenior💰 $210,000 per yearWebsite

Tech Stack

Tools & technologies

Amazon RedshiftBigQueryCloud

About the role

Key responsibilities & impact

Own the data platform strategy end-to-end — from ingestion architecture and scalable, multi-tenant data infrastructure to transformation pipelines, a modern BI layer, and how the platform grows as we add clients and data sources.
Drive the AI-on-data layer. Partner with our AI engineer to define how agents and LLMs access, query, and act on platform data — semantic models, retrieval patterns, and in-warehouse AI primitives.
Build and productize our integrations motion — ingesting data from a growing set of first- and third-party sources. Turn what today requires custom work into a repeatable, operable pattern.
Lead and develop our data engineering team. You'll manage directly, set technical direction, and raise the bar on quality — starting with a tight-knit team with room to grow.
Define the engineering standards for the data org — CI/CD, testing, infra-as-code, data lineage, governance, and observability — so the platform scales without fragility.
Be a strong hands-on presence on the warehouse and transformation layer — fluent enough in Snowflake to contribute meaningfully alongside the team, not just oversee it.
Partner with go-to-market teams to define what great looks like for client onboarding and data delivery, and drive engineering execution against that bar.

Requirements

What you’ll need

8+ years in data engineering, with at least 2 years in a leadership role
Strong people management instincts: clear communicator, good at developing talent, comfortable giving direct feedback, and able to build a high-performance culture even with a small team.
Hands-on experience with a modern cloud data warehouse and transformation stack — Snowflake + dbt strongly preferred; Redshift, Databricks, or BigQuery with a fast ramp is acceptable.
Proven experience building AI on top of structured data — semantic layers, agent/LLM access patterns to warehouses, or retrieval-augmented generation.
Deep expertise in data ingestion at scale — you've built or owned the systems that pull from many disparate sources into a warehouse. You know when to use an off-the-shelf connector, when to build, and how to make either one operable at scale.
Experience building and shipping a productized, multi-tenant data offering — client isolation, onboarding flows, SLAs, and ongoing support. You think in products, not projects.
Solid engineering fundamentals: version control, code review, CI/CD, infra-as-code — and a bias toward standards that teams can repeat, not heroics that only you can maintain.

Benefits

Comp & perks

Remote first
Competitive salary and equity
Flex PTO policy
401(k)
Generous medical, dental and vision plans
16 weeks paid parental leave for primary and secondary caregivers
$1,000 reimbursement for work-from-home tech setup
Company-paid sustainability subscription to ensure carbon neutrality is maintained for employee activities, such as travel

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

data engineeringdata ingestionAI on structured datasemantic modelstransformation pipelinesCI/CDinfra-as-codedata lineagegovernanceobservability

Soft Skills

people managementclear communicationtalent developmentdirect feedbackhigh-performance cultureleadershipteam managementcollaborationclient onboardingengineering execution