Senior Data Engineer

NxT Level•New York, NY

22d

About The Position

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

Requirements

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

Nice To Haves

Experience in 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

Responsibilities

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: Determine how sensitive healthcare data is handled, including row- and column-level access controls, PHI restrictions, HIPAA-aligned data architecture, Safe Harbor anonymization, data deletion workflows, role-based access policies, and 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.