Senior Data Engineer, Data Foundations

Natera

2h • $125,000 - $156,300 • Remote

About The Position

Natera is hiring a hands-on senior individual contributor for its Data Foundations team. The role builds governed, trusted, reusable Data 360 products—such as Patient, Provider, Order, and Clinical Result—that serve as a single source of truth for core business metrics and underpin analytics, dashboards, and AI solutions. The engineer writes production code, makes foundational design and architecture decisions, and helps establish engineering standards across data domains.

The team is AI-native, and the engineer is expected to use AI coding assistants and agentic tools throughout design, development, testing, documentation, and operations, while helping scale effective practices and safeguards. Work takes place in a regulated environment where data quality, lineage, privacy, and compliance are essential. The role collaborates with engineering, lab operations, product, commercial, governance, security, and platform teams, and requires clear communication and the ability to build alignment without direct authority.

Requirements

• At least 6 years of data or software engineering experience for Senior level, or at least 9 years for Staff level, with a track record of designing and shipping production data models and pipelines at scale.
• Strong software engineering fundamentals, including version control, testing, code review, CI/CD, modular design, and maintainable, 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, including 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 and AWS are preferred.
• Experience integrating varied data types and sources, including relational, semi-structured, event, operational, and third-party data, and defining and applying data standards.
• Experience designing and executing data-product validation, including source reconciliation, automated testing, and working with domain experts to confirm metrics reflect business reality.
• Working knowledge of data governance practices, including data contracts, lineage, cataloging, access control, data quality engineering, and certification of trusted datasets.
• Hands-on daily use of AI coding assistants and agentic tools, with an informed perspective on their effective use.
• Practical experience with AI/LLM technologies, such as LLM frameworks or managed AI services, 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.

Nice To Haves

• Experience with Snowflake semantic views, Cortex Analyst, or other semantic-modeling approaches for natural-language querying.
• Experience in healthcare, life sciences, or another regulated domain handling PHI or other sensitive data.
• Experience with laboratory information systems, LIMS, bioinformatics pipelines, or genomic data.
• Experience with workflow orchestration tools such as Airflow, Dagster, or Prefect.
• Experience with Snowflake performance tuning and cost management, and features such as dynamic tables, Snowpark, and dynamic data masking.
• Experience with infrastructure-as-code such as Terraform and with data observability tooling.
• Experience integrating Salesforce or EHR data sources.
• Experience with Sigma or comparable BI and semantic-layer tools such as Looker, Tableau, or 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.

Responsibilities

• Design and build governed, documented dimensional and domain-driven Data 360 products integrating clinical, laboratory, operational, and commercial data.
• Define and maintain canonical definitions for core business metrics so they are calculated once, certified, and reused consistently.
• Own foundational-layer design and architecture decisions, lead design reviews, and make build-versus-buy and technology decisions with platform and architecture teams.
• Model structured, semi-structured, event, laboratory, LIMS, genomic, results, third-party, and CRM data, reconciling conflicting definitions and identifiers.
• Build and maintain semantic layers and certified trusted views that support consistent self-service analytics.
• Use Sigma to publish foundational datasets, governed metrics, and reusable workbook components.
• Design models for performance, discoverability, and usability by analysts, dashboards, and LLM-based querying.
• Build and maintain scalable, tested, documented dbt-on-Snowflake transformation pipelines using modularity, version control, code review, automated testing, and CI/CD.
• Write production-grade SQL and Python and contribute shared frameworks, templates, and macros.
• Lead root-cause analysis of complex data issues and drive durable fixes.
• Establish and enforce standards for naming, modeling, testing, documentation, and data-product certification.
• Implement data quality checks, data contracts, lineage, and monitoring.
• Ensure PHI and genomic data handling meets HIPAA, CLIA, and internal security and governance requirements, including access control, masking, semantic tagging, auditability, and retention.
• Partner with governance, security, and data platform teams on metadata, cataloging, and discoverability.
• Validate every data product before release by reconciling it to source systems, checking grain, uniqueness, and referential integrity, comparing metrics with known-good reports, confirming results with domain experts, and documenting validation.
• Use AI coding assistants and agentic tools in daily engineering work and increase personal and team throughput.
• Build governed, well-modeled data and semantic context that LLMs and agents can query accurately and safely.
• Contribute to suitable AI-enabled solutions, such as LLM-assisted classification or extraction, retrieval over curated data, and agent-ready data products.
• Help define team practices for prompts and context, reusable agent workflows, AI-assisted testing and review, and quality and compliance guardrails.
• Work with product managers, lab operations, engineering, and commercial stakeholders to translate questions into reusable data products.
• Collaborate with data and AI platform teams to build on shared foundations rather than one-off pipelines.
• Mentor engineers through code review, pairing, and design feedback, and represent the team in architecture forums and data governance discussions.

Benefits

• Comprehensive medical, dental, vision, life, and disability plans for eligible employees and their dependents.
• Free testing for employees and their immediate families.
• Fertility care benefits.
• Pregnancy and baby bonding leave.
• 401k benefits.
• Commuter benefits.
• Employee referral program.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed