Senior Data Platform Engineer

PushPress is hiring its first Data Platform Engineer to build and own the foundation for its reporting, data-driven products, and AI features. The role covers data ingestion, quality, governance, security, and access, while supporting teams in building reliable data and AI experiences for gyms.

Responsibilities:

• 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
• 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
• 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
• 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

Requirements:

• 5+ years in data engineering, data platform, data infrastructure, or software engineering roles, with production infrastructure you owned end to end
• 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
• 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
• 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
• 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