Senior Data Platform Engineer

About PushPress

PushPress is the AI-powered gym management platform built for boutique fitness.

We are transforming how boutique fitness owners operate and how the entire $100B global fitness industry connects, transacts, and grows. Trusted by more than 5,000 gyms and 500,000 members, PushPress processes ~$1B annually.

We're a Series B company backed by Altos Ventures, StepStone, and Mucker Capital, and we're going all-in on AI. Our AI Assistant, AI Member Intel, and AI-powered reporting tools are already changing how gym owners and coaches operate. We believe AI should make the human side of fitness better, not replace it.

By embedding intelligence directly into the product, we enable boutique fitness owners to make faster decisions, unlock new revenue streams, and collaborate across a connected network. As more gyms join the platform, data compounds, intelligence strengthens, and the value of the ecosystem increases for every owner.

We are a global team of builders, operators, and fitness fanatics on a mission to level the playing field for independent fitness entrepreneurs. If you are ready to help reshape an industry, let's talk!

The Role

PushPress is building a suite of data-driven products and customer experiences that powers reporting and AI features for 5,000+ gyms and our internal teams. We're hiring our first Data Platform Engineer to own the foundation that all of it runs on: how data gets in, where it lives, how it's governed, how it's extracted (while monitoring who uses it and why), and whether people and AI agents can trust it. Our direction: anyone at PushPress, and the AI agents they work with, should get answers they can rely on without waiting on the Data team. You'll design the platform that makes that possible and support the teams who build on it. You'll collaborate closely with our Data team (Data Engineering and Analytics) and the broader R&D team, and you'll have a real say in how we turn our data into tools, including AI, that help gyms run better businesses.

What You'll Own

Fast, trustworthy data for 5,000+ gyms

• Make the reporting gym owners see in PushPress fresh, fast and right, so they can act on their business in the moment

Make sure every number we run the company on, up to what the Board sees, traces back to where it came from

• Set the bar for freshness and uptime, take part in incident response, and fix root causes so problems don't come back

A full view of the customer journey

• Bring product, payment and third-party data together so we can see how gyms and their members use PushPress, from first inquiry to daily check-in

• Partner with Engineering so new features and integrations produce clean, well-structured data from day one, with breaking changes caught before they ship

• Simplify how data moves: fewer paths and tools, each with a clear owner, and costs that stay visible and in check

Privacy and security by design

• Protect the gym and member data entrusted to us: who can see what, a record of every access by a person or an AI agent, and safeguards no AI system can change

• Make honoring a privacy request routine, without breaking financial reporting or audit

The foundation for AI at PushPress

• Give our AI Assistant, AI Member Intel, and AI reporting the context they need to give gym owners useful, personal answers

• Make business definitions readable by people and AI, so a metric means the same thing in a dashboard, a chat or a Board deck

• Help every team at PushPress answer its own questions through AI, safely and with clear limits on cost and access

What We're Looking For

You've done this hands-on

• 5+ years in data engineering, data platform, data infrastructure, or software engineering roles, with production infrastructure you owned end to end (ideally at a B2B SaaS or payments company)

• A cloud warehouse experience at scale: performance tuning, cost management, access controls (RBAC, masking) and schema design

• Strong Python and SQL, with solid software engineering habits (testing, CI/CD, code review, infrastructure as code such as Terraform)

• Hands-on experience initiating, maintaining, and running an orchestrator like Airflow or Dagster in production, not just writing DAGs

• Managed ingestion tools (Hevo, Fivetran) and custom pipelines when they fall short, including at least one of CDC, streaming or event-driven

• Data quality checks and alerting you built (Monte Carlo, Elementary, or your own), with a runbook someone else can follow

• Using AI daily, including at least one workflow others rely on and a check that catches it when it's wrong

You've worked with

• A real-time analytics database such as ClickHouse, and a view on when to use it instead of a warehouse

• Privacy and governance requirements under GDPR, CCPA or PCI (access, auditing, retention and deletion)

TypeScript codebases, alongside the product engineers who produce our data

You're curious about

• Data for AI: semantic layers, retrieval, evals and…