About Perry Weather

Perry Weather is the weather safety standard for organizations that operate outdoors. We combine on-site weather hardware with a software platform that automates alerts, siren activations, and safety decisions for more than 3,000 organizations — from the PGA of America, the NFL, and MLB to Turner Construction, thousands of school districts, cities, and golf courses. We've grown 80%+ annually for five consecutive years, and are continuing to grow.

We're headquartered in Dallas at The Centrum, in the Oak Lawn neighborhood, where our teams work together every day to help organizations make faster and safer decisions when weather puts people, assets, and operations at risk.

About the Role

We're hiring our first Data Engineer to build Perry Weather's data platform from the ground up. You'll design the architecture, build it, and run it in production. You'll also be accountable for whether the company trusts and uses what you build.

Today our operational data lives across Azure SQL Server, TimescaleDB, and business systems including HubSpot, Zendesk, and Acumatica. Nothing unifies them yet. Your job is to build the lakehouse and analytics platform that does, and to make it serve three things at once: executive decision-making, internal AI workflows, and a new line of customer-facing data products.

As the first hire on the data team, you set the architecture and the standards that later hires inherit. The bar is high. We want someone who has built a data platform from zero before, has strong opinions about how it should be done, and prefers an open mandate to a defined one.

Responsibilities

Own the data platform end to end. Architecture, ingestion, modeling, orchestration, testing, cost, and reliability. You make the build-vs-buy calls and stand behind them.

Unify our operational and business systems. Bring Azure SQL Server, TimescaleDB, HubSpot, Zendesk, Acumatica, and sensor telemetry into one modeled, documented, queryable layer the company treats as the source of truth.

Own data reliability. Data contracts, freshness and quality SLAs, lineage, observability, and the on-call rotation behind them, so problems surface before they reach a dashboard.

Build the foundation for our first data products. Our platform collects weather observations, sensor telemetry, environmental conditions, alerts, and operational events. You'll build the infrastructure that turns that into customer-facing analytics, APIs, benchmarking, and historical datasets, multi-tenant and secure from the first release.

Lead our AI data strategy. Build the pipelines and centralized datasets behind internal AI agents and LLM workflows, and partner with department leaders to automate repetitive work. We want a real point of view on where AI creates durable value, grounded in systems thinking, not tool lists.

Set the standard for the team that follows. Define how we model data, review changes, and document decisions. Help us hire the next data engineers.

Work directly with the business. Finance, Revenue, CX, Product, and the executive team. Understand the decisions they're making and design for them.

Requirements

7+ years in data engineering, or backend engineering with deep data ownership.

At least one cloud-native data platform you designed, built from scratch, and operated in production yourself.

Enterprise-scale data lake, lakehouse, or warehouse experience where you owned the schema, storage, and partitioning decisions.

Expert SQL and dimensional modeling. You can design a model that survives a changing business and explain a metric definition to a non-technical executive.

Strong software engineering fundamentals: version control, testing, CI/CD, code review, and infrastructure as code.

Reliable ETL/ELT across operational databases, imperfect SaaS APIs, and telemetry streams.

A track record supporting executive reporting where the numbers carried real consequences.

Hands-on depth in a modern cloud data stack. Synapse, Databricks, or Snowflake experience all count.

Fluency with AI-native engineering tools and agentic workflows (e.g., terminal-native coding agents, LLM-assisted code refactoring and data pipeline generation) to multiply technical output and speed up development cycles.