Staff Data Engineer

About the Role

Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life-changing technologies spans the spectrum of healthcare, with leading businesses and products in diagnostics, medical devices, nutritionals, and branded generic medicines. Our 122,000 colleagues serve people in more than 160 countries. The Staff Data Engineer is a senior, hands-on technical contributor within an Enterprise Data domain, owning technical design and the most complex implementation work for assigned data products, capabilities, and integrations. This role is based in Madison, WI.

Responsibilities

• Own technical design and hands-on delivery for the most complex or highest-risk work across assigned data products, capabilities, and initiatives, including data models, pipeline architecture, integration patterns, and platform usage decisions.
• Produce design documentation that allows others to understand, review, and build on technical decisions, and drive design review with domain and cross-domain peers.
• Build and evolve assigned domain data products so they are documented, discoverable, governed, observable, and supported throughout their lifecycle for reuse across analytics, semantic layers, machine learning, and AI applications.
• Define and uphold data contracts for the products the domain publishes, including schemas, freshness and availability expectations, breaking-change policy, and producer and consumer responsibilities.
• Design data products for AI and machine learning consumption with reproducibility, lineage, timeliness, and defined data quality controls appropriate to the use case.
• Diagnose and resolve complex production and data quality issues, and drive root cause resolution rather than recurring remediation.
• Use approved AI-assisted development tooling where appropriate to accelerate engineering work, validating output against correctness, security, privacy, and quality standards.
• Own the technical cost efficiency of assigned data products, including compute and warehouse sizing, job and query optimization, serverless and storage tradeoffs, and cost-to-serve implications.
• Evaluate and recommend tools, patterns, and platform capabilities within enterprise standards, raising cases where an exception may be warranted.
• Work directly with business stakeholders to understand needs, clarify requirements, and shape technical solutions.
• Communicate technical concepts, tradeoffs, constraints, and delivery implications clearly to non-technical audiences.
• Participate in technical working sessions with partner organizations on integration and design questions affecting the domain.
• Surface scope, priority, resourcing, and technical-debt implications of technical decisions for leadership review.
• Serve as a technical lead for assigned domain capabilities and initiatives, guiding engineers through design and implementation.
• Provide code and design review to raise quality and consistency across the domain team.
• Mentor engineers and support their technical growth.
• Engage Principal Engineers on cross-domain architecture patterns and enterprise standards.
• Lead adoption and continuous improvement of domain testing, observability, CI/CD, and delivery practices.
• Ensure solutions meet requirements for quality, reliability, scalability, performance, observability, security, privacy, access, and lifecycle management.
• Design and implement appropriate handling of protected health information and other sensitive data, consistent with HIPAA, CLIA, and enterprise privacy standards.
• Serve as domain subject matter expert for significant production incidents.
• Uphold company mission and values through accountability, innovation, integrity, quality, and teamwork.
• Support and comply with the company's Quality Management System policies and procedures.
• Maintain regular and reliable attendance.
• Act with an inclusion mindset and model these behaviors for the organization.

Qualifications

• Bachelor's degree in Data Science, Computer Science, Information Systems, Mathematics, or Engineering.
• 8+ years of progressively responsible experience in data engineering or a closely related discipline.
• Demonstrated experience owning technical design and hands-on delivery for complex data products, pipelines, or platform capabilities.
• Advanced SQL and Python, with experience in relational, dimensional, and semi-structured data modeling.
• Experience with scalable, reliable distributed data systems on platforms like Databricks (Spark) and cloud services (AWS, Azure, GCP).
• Experience with version control, automated testing, CI/CD, and orchestration tools (Databricks Workflows, Delta Live Tables, Airflow, dbt).
• Experience delivering governed data using catalog, lineage, access control, and data quality tools (Unity Catalog).
• Ability to work with business stakeholders, mentor engineers, and raise technical quality across a team.

Skills

• Streaming and event-driven architectures (Kafka).
• Regulated environments (HIPAA, CLIA, SOX, FDA, ISO 13485, IEC 62304), especially in life sciences or clinical data handling.
• Semantic layers, metric definitions, data products, or feature pipelines for analytics, ML, and AI.
• Open data formats (Parquet, Avro, Delta Lake).
• REST API development and integration patterns.
• Business intelligence concepts and semantic consumption patterns (Tableau).

Pay

The base pay for this position is $99,300.00 – $198,700.00. In specific locations, the pay range may vary from the range posted.