Senior Data Engineer

They’re now hiring their first dedicated Data Engineer to build the data foundation behind the company.

This is not a role where you inherit a mature platform and optimize around the edges.

You’ll own how data moves through the business from end to end - from production systems into the lakehouse and warehouse, through transformation and governance, and ultimately into the hands of AI, product, finance, partnerships, and leadership.

If you’ve wanted the opportunity to define how a company thinks about data from the ground up, this is it.

What You’ll Own

Build the Data Platform

• Design and operate reliable CDC and ELT pipelines from MariaDB, PostgreSQL, and MongoDB into S3, Apache Iceberg, and Snowflake
• Create a governed, trusted source of production data that the entire company can build on
• Design a scalable warehouse architecture with clean raw, transformed, and business-ready layers
• Implement monitoring, alerting, and reliability standards across the data stack

Create Trusted Business Data

• Build the transformation layer using dbt or similar tooling
• Turn raw production data into tested, documented, version-controlled models
• Establish trusted definitions for metrics such as:
• Visits
• Bookings
• Revenue
• Retention
• Product engagement
• Power executive reporting and downstream analytics from a consistent source of truth

Own Orchestration & Reliability

• Select and implement the right orchestration platform for the company
• Automate pipelines, transformations, and dashboard refreshes
• Build monitoring and alerting so failures are caught quickly
• Establish reliability standards as the volume and complexity of the platform grows

Build Healthcare-Grade Data Governance

You’ll play a critical role in determining how sensitive healthcare data is handled.

That includes:

• Row- and column-level access controls
• PHI restrictions
• HIPAA-aligned data architecture
• Safe Harbor anonymization
• Data deletion workflows
• Role-based access policies
• Secure datasets for analytics and AI use cases

The goal is to make data highly useful without compromising patient privacy or security.

Enable AI & Product Teams

• Build datasets and pipelines supporting AI model training and evaluation
• Partner with AI engineers on training data and data quality
• Support product teams with trustworthy behavioral and product data
• Help finance, marketing, partnerships, and leadership answer important business questions without creating separate versions of the truth, This role is best suited for someone who:
• Likes building systems from scratch
• Doesn’t need a perfectly defined roadmap before getting started
• Can evaluate tools rather than simply use whatever is already installed
• Thinks about reliability, governance, and maintainability from day one
• Can translate business questions into durable data models
• Communicates well with technical and non-technical stakeholders
• Wants meaningful ownership instead of narrowly scoped tickets
• Enjoys being the person people turn to when the answer starts with, “What does the data actually say?”

• 5+ years of data engineering experience
• Experience owning production data infrastructure end to end
• Strong SQL and Python
• Experience building and maintaining CDC / ELT pipelines
• Familiarity with tools such as Fivetran, Airbyte, or similar platforms
• Hands-on experience with modern warehouse or lakehouse architectures
• Experience with:
• AWS S3
• Apache Iceberg
• Snowflake or similar data warehouses
• Data catalogs
• dbt or comparable transformation frameworks
• Experience with orchestration platforms such as Airflow, Dagster, or AWS Glue
• Strong AWS fundamentals including IAM, Lambda, Kinesis, and Glue
• Strong understanding of production reliability and data quality, * HIPAA, PHI, or healthcare data
• Healthcare technology
• Data anonymization and governance
• ML training datasets and feature pipelines
• SageMaker, Databricks, or Jupyter environments
• ClickHouse or high-volume event pipelines
• Server-side tracking, CDPs, or behavioral analytics
• BI tooling such as Metabase
• Semantic or metrics layers
• First data engineer or early-stage startup experience

Healthcare experience is helpful, but the bigger requirement is that you’ve built reliable, governed data infrastructure in production.

Base Salary: $200,000 - $275,000 Equity: Meaningful ownership based on experience and level

This is an opportunity to become the technical owner of the data foundation behind a rapidly scaling AI healthcare company.

Our client is building an AI-powered healthcare platform designed to make high-quality care more accessible at massive scale.