Staff Data Platform Engineer

Tilt runs on data-driven decisions, and our Data Platform is what makes that possible. Over the last two years our team built the governed, queryable Databricks warehouse the company now operates on, plus the dbt gold layer that turns raw data into something teams can actually use. Today the company can't build models, perform analytics or measure anything without us.

The job ahead isn't building the warehouse; it's scaling how we extend it so Analytics, Data Science, and app can move fast without bottlenecking on us. Every time one of those teams waits on a ticket, the whole company's decision-making slows down. Our job is to make that wait disappear.

As our Staff Data Platform Engineer, you'll be the technical lead: a senior individual contributor who sets the architecture and builds the patterns, paved roads, and self-service flows that let other teams build on the platform safely — without filing a ticket and waiting. You won't be measured by the pipelines you personally ship, but by the leverage and reliability the platform gives everyone else.

We work in Python to build tooling for loading Databricks, Azure Event Hub, and Terraform, and we operate pattern-first: every project leaves a reusable system behind.

Tilt is a remote-first company. We drive connectivity through regular company offsites. Travel for company offsites is expected at a minimum 2 times a year.

- Design the technical architecture of the Databricks Data Warehouse — ingestion, storage, compute, orchestration, governance, and observability — and act as the pattern reviewer and architectural sounding board for the team.

- Build, scale, and optimize secure, self-service frameworks for batch and streaming data so the same request shape is never solved by hand twice.

- Treat the platform like production software: define and defend SLOs, own lineage and observability, and lead incident triage and root-cause analysis to durable fixes.

- Design governance and multi-tenancy as first-class guardrails — least-privilege access, auditing, cost controls, and privacy-aware patterns — across app-team schemas and multiple workspaces.

- Automate and harden data infrastructure and CI/CD so the warehouse stays safe to change at scale.

- Raise the team’s technical bar through design review, code review, and mentorship, setting standards that are adopted because they’re good.

- Make documentation a first-class deliverable — every pattern ships with the docs that let an analyst, partner team, or AI agent use it without asking us.

- 8+ years of data engineering / data platform experience, including significant time as a senior IC building reusable infrastructure that other teams depend on at scale.

- Demonstrated technical leadership — setting architecture direction, reviewing designs, and mentoring engineers on a data platform team.

- Strong software-engineering fundamentals in Python (or another Object Oriented Language) — production-grade code, testing, CI/CD, and Infrastructure-as-Code (Terraform or similar) on a cloud provider stack (Azure, AWS, or GCP).

- Experience with Terraform or similar Infrastructure-as-Code (IaC) tools, and a cloud provider stack (AWS, GCP, or Azure).

- Data governance, security, and multi-tenancy competence — least-privilege access, auditing, lineage, and privacy-aware design.

- Able to effectively leverage AI-powered development tools (e.g., Claude, Augment) to enhance productivity, code quality, and collaboration.