Staff Data Engineer

Abbott (abbottcareers2 board); Madison (WI); On-site; USD 120,000 - 180,000; Full time

Job summary

Abbott in Madison, WI seeks a Staff Data Engineer to own complex data product design and hands-on delivery within the Enterprise Data domain. The role bridges technical execution and business context, guiding design and ensuring durable, scalable solutions.

You will mentor engineers, review designs, and drive best practices across data contracts, observability, and cost efficiency. Relocation assistance may be available for qualified candidates.

Responsibilities

• Own technical design and hands-on delivery for the most complex or highest-risk data work across data products and initiatives.
• Produce design documentation to communicate decisions and drive design reviews.
• Build and evolve data products for analytics, semantic layers, ML, and AI applications.
• Define and uphold data contracts including schemas, freshness, availability, and responsibilities.
• Design data products for AI/ML with reproducibility, lineage, timeliness, and data quality controls.
• Diagnose and resolve complex production and data quality issues and drive root cause resolution.

Job description

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.

JOB DESCRIPTION

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. The role establishes, communicates, and evolves the technical approach for the work it leads and is accountable for the quality, durability, and supportability of the solutions it shapes.

Staff Data Engineers work directly with business stakeholders to understand needs and shape technical solutions, engaging at a level appropriate to the work they lead. The role is expected to be fluent in both technical execution and business context, translating between them without losing precision in either.

The Staff Data Engineer sets technical direction for assigned capabilities and initiatives within the domain, applies enterprise standards and platform patterns, engages Principal Engineers where cross-domain considerations apply, and multiplies the effectiveness of the domain team through design leadership, code review, and mentoring.

This role is based in Madison, WI. Relocation assistance may be available for qualified candidates.

Essential Duties

Technical design and solutioning

• 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.

Stakeholder engagement

• 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 such as Software Engineering, IT Applications, and Enterprise Architecture on integration and design questions affecting the domain.
• Surface scope, priority, resourcing, and technical‑debt implications of technical decisions for leadership review rather than resolving them independently.

Technical leadership and multiplication

• Serve as a technical lead for assigned domain capabilities and initiatives, guiding engineers through design and implementation without formal authority.
• Provide code and design review that raises quality and consistency across the domain team.
• Mentor engineers and support their technical growth, including engineers transitioning into data engineering from adjacent disciplines.
• Engage Principal Engineers on cross‑domain architecture patterns and enterprise standards, and contribute domain perspective to initiatives that extend beyond the domain.
• Lead adoption and continuous improvement of domain testing, observability, CI/CD, and delivery practices within enterprise standards.

Operational excellence and compliance

• Ensure delivered 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, including access control, masking and de-identification, lineage,