Position Overview

The Principal Data Engineer (IC) is a senior individual contributor and the accountable technical leader for assigned cross-domain initiatives and enterprise data engineering capabilities. The role owns integrated technical direction and technical outcomes for work spanning multiple data domains, defines and stewards enterprise engineering standards and reference architectures, and drives convergence where duplicated or inconsistent solutions create enterprise cost, risk, or operational burden. The role advises on scope, sequencing, capacity, dependencies, and technical debt, but does not independently commit domain resources or business delivery dates. This position has no people-management responsibility.

Enterprise Data operates a domain-aligned model built on Databricks and Unity Catalog. Working with Domain Leaders, Staff Engineers, Platform Engineering, and partner organizations, the role converts ambiguous enterprise needs into executable architecture and carries the most complex or highest-risk work through validation and production. The role remains hands-on through prototyping, reference implementations, critical-path development, design and code review, and production problem solving.

This role is based in Madison, WI.

Essential Duties

Include, but are not limited to, the following:

Cross-domain technical leadership and delivery

• Own the technical outcome of assigned cross-domain initiatives from initial ambiguity through production and lifecycle support.
• Convert enterprise needs into integrated architecture and executable technical plans, decomposing complex work into deliverable increments across domains.
• Coordinate technical execution across domain Staff Engineers and partner teams, identifying dependencies and architectural risks, and escalating decisions requiring business or delivery authority.
• Remain hands-on through prototypes, reference implementations, critical-path development, technical validation, and production problem solving.

Enterprise architecture and standards

• Define, steward, and evolve enterprise data engineering standards, patterns, and reference architectures, and drive convergence where inconsistent or duplicative implementations create enterprise cost, risk, or operational burden.
• Lead architecture for assigned enterprise capabilities, including semantic and metrics layers, canonical data models, batch, API, event-driven, and streaming patterns, and governed data products supporting analytics, machine learning, and AI.
• Establish enterprise data-contract standards covering schemas, service expectations, compatibility, breaking-change policy, and producer-consumer responsibilities, and partner with Platform Engineering to convert recurring cross-domain needs into shared capabilities.

Technical leadership and mentorship

• Lead enterprise design and code reviews for high-complexity or cross-domain work, and coach and mentor Staff and Senior Engineers across domains without formal people authority.
• Communicate architecture and technical tradeoffs clearly to engineering, business, and executive stakeholders, and build reusable guidance that increases engineering consistency across Enterprise Data.

Operational excellence, security, and efficiency

• Evaluate architecture tradeoffs across reliability, scalability, performance, security, privacy, operability, adoption, and technical cost, and drive cost-efficient use of compute, storage, streaming, and orchestration.
• Serve as the enterprise technical escalation for incidents involving multiple domains or shared architecture patterns, and lead root-cause analysis and preventive changes for recurring enterprise issues.
• Design and review architectures handling protected health information to meet applicable security, privacy, lineage, audit, Quality Management System, HIPAA, CLIA, and regulatory requirements.
• Provide technical direction and due diligence for vendor and external-partner solutions, and advance responsible engineering practices including approved AI-assisted development capabilities.
• Ability to work nights and/or weekends, as needed.

Minimum Qualifications

• Bachelor's Degree in Data Science, Computer Science, Information Systems, Mathematics, or Engineering.
• Expert-level experience with software development design and development and with relevant domain specific skills (see below).
• Spark on Databricks or comparable platforms; Python, Scala, SQL, and Snowflake experience.
• ETL and ELT data pipelines, including batch and event-driven patterns.
• Designing and implementing data modeling solutions using relational, dimensional, and/or NoSQL databases.
• Database architecture testing methodology, including execution of test plans, debugging, and testing scripts and tools.
• Open data file and table formats (Parquet, Avro, Delta Lake); cloud infrastructure and delivery services (AWS, S3, SQS, and GitLab CI/CD).
• REST API development; familiarity with BI concepts and Tableau performance considerations.
• Agile development tools; including, but not limited to, JIRA, Confluence repository.
• Demonstrated ability to lead through influence across multiple teams and communicate complex technical decisions to senior engineering, business, and executive stakeholders.
• Demonstrated ability to perform the essential duties of the position with or without accommodation.

Preferred Qualifications

• Databricks, Apache Spark, Delta Lake, and Unity Catalog at enterprise scale.
• Kafka, change data capture, and production event-streaming architectures.
• Semantic or metrics layer design, canonical data models, and governed data products.
• Cloud data architecture in AWS, Azure, or Google Cloud Platform.
• Life sciences, diagnostics, or clinical laboratory environments involving protected health information, HIPAA, CLIA, FDA, or Quality Management System requirements.
• Technical assessment and architecture direction for vendor and external-partner platforms.

The base pay for this position is

In specific locations, the pay range may vary from the range posted.