Senior Data Platform Engineer

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

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 giving agents governed access to data
• Data contracts with the teams that produce data
• Event platforms or CDPs (Segment, RudderStack, Snowplow, PostHog) and identity resolution across product, billing and CRM data

How you work

• You're curious about the gyms and members behind the data
• You tackle messy data head on, and you find out what changed upstream
• You explain tradeoffs in plain terms, and can say "not yet, and here's why"
• You improve as you go: docs, runbooks and alerts are standard practice

• Equity Incentive Plan
• Flexible Paid Time Off (and Public Holidays)
• Health Care Plan (Medical, Dental & Vision)
• Retirement Plan (Company Sponsored 401k with Match)
• Life Insurance
• Family Leave (Maternity, Paternity)
• Work From Home Stipend
• Wellness Stipend