The Principal Data Engineer serves as a senior individual contributor and accountable technical leader for cross-domain enterprise data engineering initiatives at Abbott Laboratories. The role defines and drives engineering standards and reference architectures, leads complex high-risk technical work through validation and production, and remains hands-on across critical delivery and lifecycle support.

Key Responsibilities

Own the technical outcome of assigned cross-domain initiatives, progressing from early ambiguity through production delivery and ongoing lifecycle support.
Translate enterprise needs into integrated architectures and executable technical plans, breaking down complex efforts into incremental deliverables across multiple domains.
Coordinate technical execution with Staff Engineers and partner teams, identifying dependencies and architectural risks, and escalating decisions that require business or delivery authority.
Remain hands-on across prototypes, reference implementations, critical-path development, technical validation, and production issue resolution.
Define, steward, and evolve enterprise data engineering standards, reusable patterns, and reference architectures, driving convergence where inconsistent or duplicative implementations increase enterprise cost, risk, or operational burden.
Lead architecture for enterprise capabilities, including semantic and metrics layers, canonical data models, and batch, API, event-driven, and streaming patterns, while supporting governed data products for analytics, machine learning, and AI.
Establish enterprise data-contract standards covering schemas, service expectations, compatibility, breaking-change policy, and producer-consumer responsibilities, partnering with Platform Engineering to turn recurring cross-domain needs into shared capabilities.
Lead enterprise design and code reviews for high-complexity or cross-domain work, and coach and mentor Staff and Senior Engineers through influence without formal people-management authority.
Communicate architectural tradeoffs clearly to engineering, business, and executive stakeholders, and create reusable guidance to improve consistency across Enterprise Data.
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.
Act as an enterprise technical escalation point for incidents involving multiple domains or shared architectural patterns, leading root-cause analysis and preventive improvements for recurring issues.
Design and review architectures handling protected health information to meet security, privacy, lineage, audit, Quality Management System, HIPAA, CLIA, and other regulatory requirements.
Provide technical direction and due diligence for vendor and external-partner solutions, and support responsible engineering practices including approved AI-assisted development capabilities.
Ability to work nights and/or weekends as needed.

Required Qualifications

Bachelor’s Degree in Data Science, Computer Science, Information Systems, Mathematics, or Engineering.
Expert-level experience with software development design and development, including relevant domain-specific skills.
Spark on Databricks or comparable platforms, with experience in Python, Scala, SQL, and Snowflake.
ETL and ELT data pipeline experience, including batch and event-driven patterns.
Experience designing and implementing data modeling solutions using relational, dimensional, and/or NoSQL databases.
Database architecture testing methodology experience, including executing test plans and debugging with testing scripts and tools.
Experience with open data file and table formats (Parquet, Avro, Delta Lake), plus cloud infrastructure and delivery services (AWS, S3, SQS, GitLab CI/CD).
REST API development experience, including familiarity with BI concepts and Tableau performance considerations.
Agile development tooling, including JIRA and Confluence repository.
Demonstrated ability to lead through influence across multiple teams and communicate complex technical decisions to senior engineering, business, and executive stakeholders.
Ability to perform the essential duties of the position with or without accommodation.

Technologies

Databricks, Unity Catalog, Spark, Python, Scala, SQL, Snowflake
ETL, ELT, Parquet, Avro, Delta Lake
AWS, S3, SQS, GitLab CI/CD
REST API, Tableau
JIRA, Confluence, Kafka, change data capture, NoSQL
HIPAA, CLIA, FHIR, GitLab, Azure, Google Cloud Platform

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 experience across AWS, Azure, or Google Cloud Platform.
Experience in life sciences, diagnostics, or clinical laboratory environments involving protected health information and compliance requirements including HIPAA, CLIA, FDA, or Quality Management System.
Experience providing technical assessment and architecture direction for vendor and external-partner platforms.

Position Overview

The Principal Data Engineer (IC) is a senior individual contributor and accountable technical leader for assigned cross-domain initiatives and enterprise data engineering capabilities. This role owns integrated technical direction and outcomes for work spanning multiple data domains, defines and stewards enterprise engineering standards and reference architectures, and drives convergence when duplicated or inconsistent solutions create enterprise cost, risk, or operational burden. The role advises on scope, sequencing, capacity, dependencies, and technical debt, without independently committing domain resources or business delivery dates. This position has no people-management responsibility.

Location and Compensation

Location: Madison, WI (onsite)

Salary: USD 129,300 - 258,700 per year

Base Pay Range: $129,300.00 – $258,700.00

Principal Data Engineer