Data Infrastructure Engineer

1 in 4 people in the US have a treatable mental health condition, but most providers don't accept insurance, making therapy too expensive for most people.
Headway’s mission is to fix this by building a new mental healthcare system everyone can access. We started by solving the biggest barrier to care: insurance. The admin work – credentialing, claims, payment reconciliation – is a nightmare. We've automated that.

But we're going further. Over 75,000 providers across all 50 states run their practice on our software, serving over 1 million patients. We are building the best tools for therapists to run their entire practice, reimagining the experience of finding a therapist, and investing in the platform foundations to enable this  aren't just a billing layer; we are becoming the platform where care actually happens.

We’re a Series D company with $325M+ in funding (a16z, Accel, Spark Capital, etc.), looking for exceptional people to help us achieve this mission. We want your time here to be the most meaningful experience of your career. Join us, and help change mental healthcare for the better.

Building a new mental healthcare system at Headway is only possible because of the scale and leverage that software and data can provide. The Data Platform team is a group of Data Engineers and Data Infrastructure Engineers who build and own our Snowflake data warehouse, data ingestion tooling, AWS infrastructure, CI/CD, and data developer experience. As we scale, we're looking for a Staff Data Infrastructure Engineer to architect, lead, and evolve the foundational systems that power our entire data organization, including new AI‑driven workflows being adopted by non‑technical staff.

You'll serve as a technical anchor across our Data Analytics & Engineering, Product Engineering, and Machine Learning teams. This is not an analytics role.

A technical leader who combines deep infrastructure expertise with the organizational instincts to drive alignment across teams. You thrive in ambiguity, operate with a high degree of ownership, and are as comfortable setting technical direction as you are building. You make other engineers better through code, architecture review, documentation, and mentorship.

• 10+ years as a Data Platform Engineer, Software/Infrastructure Engineer specializing in data, or Data Engineer in a high‑code, high‑scale environment
• Track record of driving technical strategy and cross‑functional alignment, including defining roadmaps for a data platform or infrastructure domain
• Deep expertise building full‑stack data platforms: data warehousing, ingestion pipelines, orchestration, monitoring and alerting, CI/CD, developer tooling, cloud infrastructure, and third‑party integrations
• Architectural fluency in warehouse design patterns, performance tuning, cost management, and permissions strategies on MPP analytics databases (Snowflake strongly preferred; Databricks, Big Query, or Redshift also relevant)
• Experience leading technical initiatives end‑to‑end across multiple teams and codifying engineering standards
• Proficiency designing and operating cloud infrastructure at scale (AWS preferred) using infrastructure‑as‑code (Terraform, Pulumi, AWS CDK)
• Strong Python engineering skills; solid SQL foundations; comfort with distributed systems
• Experience maintaining and scaling pipeline orchestration infrastructure (Airflow/Astronomer preferred)
• Mentorship track record growing junior and mid‑level engineers

• Agentic Data Engineering (automating data infrastructure tasks, auditing permissions, optimized CI)
• APM and observability tooling (Data Dog, New Relic) and data quality/observability frameworks
• ETL/ELT best practices at scale
• Data security and compliance in regulated environments, especially PHI
• Greenfield platform development in a high‑growth startup
• Docker, Git Hub Actions, dbt, Spark
• Build vs. buy decision‑making and vendor evaluation

• Set technical direction for data infrastructure at a company redefining access to mental healthcare
• Serve as a force multiplier, making data engineers, analysts, and ML…