Staff Data Engineer

Abbott•Madison, WI

1d•Onsite

About The Position

The Staff Data Engineer is a senior, hands-on technical contributor within an Enterprise Data domain. The role owns technical design and complex implementation work for assigned data products, capabilities, and integrations, and is accountable for the quality, durability, and supportability of the solutions it shapes. The engineer establishes and communicates technical approaches, works directly with business stakeholders to translate needs into technical solutions, and applies enterprise standards and platform patterns. The position provides technical direction for assigned work, engages Principal Engineers on cross-domain matters, and strengthens the domain team through design leadership, code review, and mentoring. The role designs, builds, governs, and supports reusable data products and pipelines throughout their lifecycle, including data contracts, AI/ML-ready data, production support, data quality, security, privacy, and cost efficiency. It collaborates with partner organizations on integration and design decisions and leads continuous improvement of engineering practices. The position is based in Madison, Wisconsin; relocation assistance may be available for qualified candidates. The role includes travel of 10% of the time and is listed as a standard work shift.

Requirements

Bachelor’s degree in Data Science, Computer Science, Information Systems, Mathematics, or Engineering.
At least 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 from design through production support.
Advanced SQL and Python skills, with experience in relational, dimensional, and semi-structured data modeling.
Experience designing and operating scalable, reliable distributed data systems on a modern data platform such as Databricks, including Spark.
Experience using cloud infrastructure services such as AWS, Azure, or Google Cloud Platform.
Experience with version control, automated testing, and CI/CD practices.
Experience with orchestration and transformation tools such as Databricks Workflows, Delta Live Tables, Airflow, or dbt.
Experience delivering governed data using catalog, lineage, access control, and data quality capabilities such as Unity Catalog.
Ability to work directly with business stakeholders to translate needs into technical solutions, mentor engineers, and raise technical quality across a team.
Ability to perform the essential duties of the position with or without accommodation.

Nice To Haves

Experience with streaming and event-driven architectures such as Kafka.
Experience in a regulated environment such as HIPAA, CLIA, SOX, FDA, ISO 13485, or IEC 62304, ideally in life sciences, diagnostics, or clinical laboratory data handling protected health information.
Experience with semantic layers, metric definitions, data products, or feature pipelines supporting production analytics, machine learning, and AI use cases.
Experience with open data file and table formats such as Parquet, Avro, and Delta Lake.
Experience with REST API development and integration patterns.
Familiarity with business intelligence concepts and semantic consumption patterns, including how data models and platform design affect performance in tools such as Tableau.

Responsibilities

Own technical design and hands-on delivery for complex or high-risk data products, capabilities, and initiatives, including data models, pipeline architecture, integration patterns, and platform decisions.
Produce design documentation and lead design reviews with domain and cross-domain peers.
Build and evolve domain data products that are documented, discoverable, governed, observable, and supported throughout their lifecycle for reuse in analytics, semantic layers, machine learning, and AI applications.
Define and uphold data contracts, including schemas, freshness and availability expectations, breaking-change policies, and producer and consumer responsibilities.
Design data products for AI and machine learning consumption with reproducibility, lineage, timeliness, and appropriate data quality controls.
Diagnose and resolve complex production and data quality issues, addressing root causes.
Use approved AI-assisted development tooling where appropriate and validate results for correctness, security, privacy, and quality.
Own technical cost efficiency, 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 potential exceptions when warranted.
Work with business stakeholders to understand needs, clarify requirements, and shape technical solutions.
Communicate technical concepts, tradeoffs, constraints, and delivery implications to non-technical audiences.
Participate in technical working sessions with Software Engineering, IT Applications, and Enterprise Architecture on integration and design questions.
Surface scope, priority, resourcing, and technical-debt implications for leadership review.
Lead assigned domain capabilities and initiatives by guiding engineers through design and implementation without formal authority.
Provide code and design reviews and mentor engineers, including those transitioning into data engineering from adjacent disciplines.
Engage Principal Engineers on cross-domain architecture patterns and enterprise standards, and contribute domain perspective to broader initiatives.
Lead adoption and continuous improvement of domain testing, observability, CI/CD, and delivery practices within enterprise standards.
Ensure solutions meet quality, reliability, scalability, performance, observability, security, privacy, access, and lifecycle-management requirements.
Design and implement handling of protected health information and other sensitive data, including access control, masking and de-identification, lineage, retention, and audit support consistent with HIPAA, CLIA, and enterprise privacy standards.
Serve as the domain subject matter expert for significant production incidents affecting domain systems.
Uphold company mission and values, support and comply with Quality Management System policies and procedures, maintain regular and reliable attendance, and model an inclusion mindset.