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Senior Data Engineer, Data Foundations
About the Role

Natera is a global leader in cell-free DNA testing, serving patients across oncology, women's health, and organ health. Our Data & AI organization builds the enterprise data platform and AI systems that turn clinical, genomic, and operational data into products that improve patient care and accelerate the business.

We are looking for a Senior or Staff Data Engineer to join the Foundations team and build governed, trusted, reusable data products (Data 360s - e.g., Patient, Provider, Order, Clinical Result) that become the single source of truth for core business metrics across Natera. This is a hands-on, senior individual contributor role. You will write production code, make design decisions that shape how data is modeled and governed across domains, and set the engineering bar for the foundational layer that every downstream analytics, dashboard, and AI solution builds on.

The Foundations team is AI-native by design. We expect engineers to use AI coding harnesses and agentic tooling as a core part of how they design, build, test, document, and operate data products, and to help the team scale those practices. You will work in a regulated environment where data quality, lineage, and privacy are core requirements of everything we ship.

The ideal candidate is a strong software engineer first, with deep data modeling instincts, a passion for standards and governance, and the judgment to balance rigor with pragmatism. You'll work across engineering, lab operations, product, and commercial teams, so clear communication and the ability to drive alignment without direct authority matter as much as technical depth.

Key Responsibilities

Foundational Data Modeling & Architecture

• 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.

Semantic Layer & Trusted Views

• Build and maintain semantic layers and certified, trusted views of core metrics that enable self-service analytics with consistent definitions.

• Use Sigma to publish foundational datasets, governed metrics, and reusable workbook components that business teams can build on with confidence.

• Design data models with downstream consumption in mind: performance, discoverability, and ease of use for analysts, dashboards, and LLM-based querying.

Hands-On Data Engineering

• Build and maintain scalable, tested, well-documented transformation pipelines in dbt on Snowflake, applying software engineering best practices: modularity, version control, code review, automated testing, and CI/CD.

• Write production-grade SQL and Python; contribute to shared frameworks, templates, and macros that make it faster and easier for the whole team to deliver new data products consistently.

• Lead root-cause analysis on complex data issues, such as metric discrepancies, event-definition mismatches, and upstream data gaps, and drive durable fixes rather than one-off patches.

Data Standards, Quality & Governance

• Establish and enforce standards for naming, modeling, testing, documentation, and certification of data products.

• Implement data quality checks, data contracts, lineage, and monitoring so downstream consumers can trust the data by default.

• Ensure data handling of PHI and genomic data meets HIPAA, CLIA, and internal security and governance requirements, including access controls, masking and semantic tagging, auditability, and retention.

• Partner with governance, security, and data platform teams on metadata, cataloging, and discoverability.

• Treat validation as part of the definition of done. Validate every data product before release by reconciling to source systems, checking grain, uniqueness, and referential integrity, comparing metrics against existing or known-good reports, and confirming results with domain experts. Document the results so consumers know what has been verified.

AI-Native Engineering

• Use AI coding assistants and agentic tools daily across design, coding, testing, documentation, and operations, and measurably increase your own and the team's throughput.

• Build the data foundations that make AI work: well-modeled, well-described, governed data and semantic context that LLMs and agents can query accurately and safely.

• Contribute to AI-enabled solutions where they fit, such as LLM-assisted classification or extraction, retrieval over curated data, and agent-ready data products.

• Help define team practices for prompt and context patterns, reusable agent workflows, AI-assisted testing and review, and the guardrails that keep quality and compliance intact.

Collaboration & Technical Influence

• Work directly with product managers, lab operations, engineering, and commercial stakeholders to understand their questions and translate them into well-scoped, reusable data products.

• Collaborate with the data platform and AI platform teams so solutions build on shared foundations rather than one-off pipelines.

• Mentor other engineers through code review, pairing, and design feedback; represent the team in architecture forums and data governance discussions.

Qualifications

Required

• 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.

Preferred

• Experience with Snowflake semantic views, Cortex Analyst, or other approaches to semantic modeling for natural-language querying.

• Experience in healthcare, life sciences, or another regulated domain handling PHI or other sensitive data.

• Experience with lab information systems, LIMS, bioinformatics pipelines, or genomic data.

• Experience with workflow orchestration (Airflow, Dagster, Prefect, or similar).

• Experience with Snowflake performance tuning, cost management, and features such as dynamic tables, Snowpark, and dynamic data masking.

• Experience with infrastructure-as-code (Terraform) and data observability tooling.

• Experience integrating with Salesforce or EHR data sources.

• Experience with Sigma, or comparable BI and semantic-layer tools (Looker, Tableau, Power BI), particularly for publishing certified datasets and governed metrics for self-service use.

• Bachelor's or Master's degree in Computer Science, Engineering, or a related quantitative field, or equivalent practical experience.

The pay range is listed and actual compensation packages are based on a wide array of factors unique to each candidate, including but not limited to skill set, years & depth of experience, certifications and specific office location. This may differ in other locations due to cost of labor considerations.
Remote USA
$125,000 — $156,300 USD

OUR OPPORTUNITY

Natera™ is a global leader in cell-free DNA (cfDNA) testing, dedicated to oncology, women’s health, and organ health. Our aim is to make personalized genetic testing and diagnostics part of the standard of care to protect health and enable earlier and more targeted interventions that lead to longer, healthier lives.

The Natera team consists of highly dedicated statisticians, geneticists, doctors, laboratory scientists, business professionals, software engineers and many other professionals from world-class institutions, who care deeply for our work and each other. When you join Natera, you’ll work hard and grow quickly. Working alongside the elite of the industry, you’ll be stretched and challenged, and take pride in being part of a company that is changing the landscape of genetic disease management.

WHAT WE OFFER

Competitive Benefits - Employee benefits include comprehensive medical, dental, vision, life and disability plans for eligible employees and their dependents. Additionally, Natera employees and their immediate families receive free testing in addition to fertility care benefits. Other benefits include pregnancy and baby bonding leave, 401k benefits, commuter benefits and much more. We also offer a generous employee referral program!

For more information, visit www.natera.com .

Natera is proud to be an Equal Opportunity Employer. We are committed to ensuring a diverse and inclusive workplace environment, and welcome people of different backgrounds, experiences, abilities and perspectives. Inclusive collaboration benefits our employees, our community and our patients, and is critical to our mission of changing the management of disease worldwide.

All qualified applicants are encouraged to apply, and will be considered without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, age, veteran status, disability or any other legally protected status. We also consider qualified applicants regardless of criminal histories, consistent with applicable laws.

If you are based in California, we encourage you to read this important information for California residents.

Link: https://www.natera.com/notice-of-data-collection-california-residents/

Please be advised that Natera will reach out to candidates with a @ natera.com email domain ONLY. Email communications from all other domain names are not from Natera or its employees and are fraudulent. Natera does not request interviews via text messages and does not ask for personal information until a candidate has engaged with the company and has spoken to a recruiter and the hiring team. Natera takes cyber crimes seriously, and will collaborate with law enforcement authorities to prosecute any related cyber crimes.

For more information:
- BBB announcement on job scams
- FBI Cyber Crime resource page

Requirements found here choose your questions.

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Requirements found

Writing Sql (must) Python (must) Compliance Salesforce (nice) Tableau (nice) Power Bi (nice)

What this posting asks for

Level: Senior — The title says "Senior".

Main responsibilities

Foundational Data Modeling & Architecture
Design and build the Data 360 data products: governed, well-documented, dimensional and domain-driven models that integrate clinical, laboratory, operational…
Define and maintain canonical definitions for core business metrics (e.g., volume, turnaround time, first-pass yield) so they are calculated once, certified,…
Own design and architecture decisions for the foundational layer; lead design reviews and make build-vs-buy and technology decisions in partnership with the …
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 …
Semantic Layer & Trusted Views
Build and maintain semantic layers and certified, trusted views of core metrics that enable self-service analytics with consistent definitions.
Use Sigma to publish foundational datasets, governed metrics, and reusable workbook components that business teams can build on with confidence.
Design data models with downstream consumption in mind: performance, discoverability, and ease of use for analysts, dashboards, and LLM-based querying.
Hands-On Data Engineering

Competencies an interviewer will look for

Technical skill Core
In the posting: “You will write production code, make design decisions that shape how data is modeled and governed across domains, and set the engineering bar for the foundat…” · “We expect engineers to use AI coding harnesses and agentic tooling as a core part of how they design, build, test, document, and operate data products, and t…” · “The ideal candidate is a strong software engineer first, with deep data modeling instincts, a passion for standards and governance, and the judgment to balan…”
Show it by: Names the tools, languages or systems used and how; Explains a technical choice and its trade-off.
Communication Core
In the posting: “You'll work across engineering, lab operations, product, and commercial teams, so clear communication and the ability to drive alignment without direct autho…” · “Establish and enforce standards for naming, modeling, testing, documentation, and certification of data products.” · “Use AI coding assistants and agentic tools daily across design, coding, testing, documentation, and operations, and measurably increase your own and the team…”
Show it by: Adapts the message to the listener; Gives an example of explaining something clearly in writing or speech.
Teamwork and collaboration Core
In the posting: “Partner with governance, security, and data platform teams on metadata, cataloging, and discoverability.” · “Collaboration & Technical Influence” · “Collaborate with the data platform and AI platform teams so solutions build on shared foundations rather than one-off pipelines.”
Show it by: Says what they did inside a team, not only what 'we' did; Shows how a disagreement was handled.
Customer focus Core
In the posting: “Natera is a global leader in cell-free DNA testing, serving patients across oncology, women's health, and organ health.” · “Our Data & AI organization builds the enterprise data platform and AI systems that turn clinical, genomic, and operational data into products that improve pa…” · “We are looking for a Senior or Staff Data Engineer to join the Foundations team and build governed, trusted, reusable data products (Data 360s - e.g., Patien…”
Show it by: Puts the customer's need in their own words; Shows a result for the customer, not only for the company.
Data and analysis Core
In the posting: “Our Data & AI organization builds the enterprise data platform and AI systems that turn clinical, genomic, and operational data into products that improve pa…” · “We are looking for a Senior or Staff Data Engineer to join the Foundations team and build governed, trusted, reusable data products (Data 360s - e.g., Patien…” · “You will write production code, make design decisions that shape how data is modeled and governed across domains, and set the engineering bar for the foundat…”
Show it by: Uses a number to describe the situation or the result; Explains what the data showed and what was done with it.
Attention to detail and quality Core
In the posting: “Natera is a global leader in cell-free DNA testing, serving patients across oncology, women's health, and organ health.” · “You will work in a regulated environment where data quality, lineage, and privacy are core requirements of everything we ship.” · “The ideal candidate is a strong software engineer first, with deep data modeling instincts, a passion for standards and governance, and the judgment to balan…”
Show it by: Gives an example of an error caught before it caused harm; Names the check or standard they used.
Stakeholder management Core
In the posting: “You'll work across engineering, lab operations, product, and commercial teams, so clear communication and the ability to drive alignment without direct autho…” · “Collaboration & Technical Influence” · “Work directly with product managers, lab operations, engineering, and commercial stakeholders to understand their questions and translate them into well-scop…”
Show it by: Shows how they aligned people with different goals; Explains how they handled a 'no' from someone senior.
Problem solving Supporting
In the posting: “Our aim is to make personalized genetic testing and diagnostics part of the standard of care to protect health and enable earlier and more targeted intervent…”
Show it by: Describes a real problem, the options considered and the one chosen; Shows how the cause was found, not just the fix.
Leadership and mentoring Supporting
In the posting: “Mentor other engineers through code review, pairing, and design feedback;”
Show it by: Describes setting direction or a standard others followed; Names someone they coached and what changed for them.
Planning and organisation Supporting
In the posting: “Our Data & AI organization builds the enterprise data platform and AI systems that turn clinical, genomic, and operational data into products that improve pa…”
Show it by: Shows how priorities were set when everything was urgent; Mentions deadlines met and how.

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Tell me about yourself. Dedicated step-by-step practice.
If an interviewer asks something improper How to respond to questions about age, family, origin or health.
Built from this posting (8):
Walk me through a piece of work where you used your strongest technical skill. What exactly did you do? From this posting
Tell me about a time you had to explain something complex to someone outside your field. From this posting
Describe a time you worked with another team to deliver something. What was your part? From this posting
Describe a time you went out of your way to solve a customer's problem. From this posting
How have you used sql in your work? Walk me through one real example. From this posting
Tell me about a problem you solved using python. From this posting
Tell me about a time you helped someone on your team get better at their work. From this posting
Tell me about a difficult problem you solved at work. How did you find the cause? From this posting
This posting reads as Software engineering: its questions come first.
Walk me through a feature you built from start to finish. What tools and languages did you use? Software engineering
Tell me about the hardest bug you have tracked down. How did you find the cause? Software engineering
Describe a technical design decision you made and the trade-off behind it. Software engineering
Tell me about a time you disagreed with a teammate in a code review. Software engineering
Production is down and you are the first engineer to notice. What do you do in the first 30 minutes? Software engineering
How do you decide what to test before you ship a change? Software engineering
Tell me about a time you helped a less experienced engineer get unstuck. Software engineering
Your estimate for a feature turns out to be badly wrong halfway through. What do you do? Software engineering
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Walk me through how you would check whether a report's numbers are right.
Describe a time you handled an upset customer or colleague.
Tell me about a time you had several deadlines at once. How did you decide what to do first?
Give an example of a mistake you caught before it caused a problem.
Tell me about a time you taught someone how to do something.
Describe a time you took the lead without being asked.
Tell me about a time you had to explain something complicated in simple terms.
Describe a process you improved.
Do you have any questions for us?
Why do you want to work here?
Why is there a gap in your employment? Difficult question
Why did you leave your last job? Difficult question
Why are you changing careers? Difficult question
What is your greatest weakness? Difficult question
What are your salary expectations? Difficult question
You seem overqualified for this role. Why do you want it? Difficult question
Improper-question practice: How old are you? / Do you have children? / Where are you from originally?
A customer is angry about a delay you did not cause. What do you do?
How do you keep track of many open customer requests?
Tell me about a time you could not give a customer what they wanted.
You find that the inventory count does not match the system. What do you do?
Tell me about a time a shipment or delivery went wrong.
How do you make sure safety rules are followed on a busy shift?
Two managers ask you to book the same room at the same time. What do you do?
How have you used spreadsheets in your work?
Tell me about a time you had to enter or check a lot of data accurately.
Tell me about a sale or agreement you are proud of.
A prospect says your price is too high. How do you respond?
Tell me about a technical problem you solved on your own.
You find a bug right before a release. What do you do?