Senior Data Engineer, Data Foundations (Remote)

Natera; United States; Remote; USD 125,000 - 156,000; Full time

Job summary

Natera is seeking a Senior or Staff Data Engineer to join the Foundations team and build governed, trusted data products that become the single source of truth for core business metrics. This hands-on role emphasizes scalable data modeling, semantic layers, and AI-native engineering practices in a regulated environment.

You will write production code, shape data across domains, and define standards for data quality, lineage, and governance while collaborating with product, lab operations, and

Qualifications

• 6+ years of data or software engineering experience (Senior) or 9+ years (Staff), with a track record of designing and shipping production data models and pipelines at scale.
• Strong software engineering fundamentals: version control, testing, code review, CI/CD, modular design, and maintainable, well‑documented code.
• Expert-level SQL and strong Python.
• Deep, hands-on experience with dbt (or an equivalent transformation framework), including testing, documentation, macros, and project structure for large, multi-domain projects.
• Strong grounding in analytical data modeling (dimensional and/or domain-driven design) and experience building semantic layers and governed metric definitions.
• Experience with a modern cloud data warehouse and cloud infrastructure (Snowflake/AWS preferred).
• Experience integrating a wide variety of data types and sources (relational, semi-structured, event, operational, and third‑party), and defining and applying data standards across them.
• Experience designing and executing validation for data products, including reconciling to source systems, building automated tests, and partnering with domain experts to confirm that metrics match business reality.
• Working knowledge of data governance practices: data contracts, lineage, cataloging, access control, data quality engineering, and certification of trusted datasets.
• Hands-on use of AI coding assistants and agentic tools in daily engineering work, with a point of view on how to use them well.
• Practical experience with AI/LLM technologies, such as building with LLM frameworks or managed AI services (e.g., Snowflake Cortex, AWS Bedrock), or preparing and structuring data for LLM and agent consumption.
• Ability to communicate clearly with technical and non-technical audiences and drive alignment in a matrixed environment.

Responsibilities

• Design and build the Data 360 data products: governed, well-documented, dimensional and domain-driven models that integrate clinical, laboratory, operational, and commercial data into consistent, trusted entities.
• Define and maintain canonical definitions for core business metrics (e.g., volume, turnaround time, first-pass yield) so they are calculated once, certified, and reused everywhere.
• Own design and architecture decisions for the foundational layer; lead design reviews and make build‑vs‑buy and technology decisions in partnership with the data platform and architecture teams.
• Model data across a wide range of sources and types, including structured relational data, semi-structured (JSON/event) data, lab and LIMS data, genomic and result data, and third‑party and CRM data, and reconcile conflicting definitions and identifiers across systems.

Skills

Advanced SQL

Python

Data modeling

Data governance

Communication

Problem solving

Mentoring

Team collaboration

Cloud data engineering

ETL design

Education

Bachelor's or Master's in CS/Engineering

Equivalent practical experience

Tools

dbt

Snowflake

Sigma

Looker

Tableau

Power BI

Airflow

Dagster

Terraform

Job description

Natera is seeking a Senior or Staff Data Engineer to join the Foundations team and build governed, trusted data products that become the single source of truth for core business metrics. This hands-on role emphasizes scalable data modeling, semantic layers, and AI-native engineering practices in a regulated environment.

You will write production code, shape data across domains, and define standards for data quality, lineage, and governance while collaborating with product, lab operations, and