Staff Data Engineer, Enterprise Data — AI/ML Ready

Abbott

Alabama

On-site

USD 99,000 - 199,000

Full time

14 days+

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Job summary

Abbott, a global healthcare leader, seeks a Staff Data Engineer in Madison, WI to own complex data product design and hands-on delivery. You will shape data pipelines, models, and platform usage, ensuring quality, observability, and cost efficiency across the domain.

You will collaborate with stakeholders, translate needs into technical solutions, mentor engineers, and drive design reviews. Strong SQL/Python, Databricks, and cloud experience are required, with emphasis on HIPAA and enterprise

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 from design through production support.
Advanced SQL and Python, with experience applying relational, dimensional, and semi-structured data modeling approaches.
Experience designing and operating scalable, reliable distributed data systems on a modern data platform such as Databricks, including Spark, and using cloud infrastructure services such as AWS, Azure, or Google Cloud Platform.
Experience with version control, automated testing, and CI/CD practices, and with orchestration and transformation tooling 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.
Demonstrated ability to work directly with business stakeholders to translate needs into technical solutions, mentor engineers, and raise technical quality across a team.
Demonstrated ability to perform the essential duties of the position with or without accommodation.

Responsibilities

Own technical design and hands-on delivery for the most complex or highest‑risk work across data products, capabilities, and initiatives.
Produce design documentation and drive design reviews with peers.
Build and evolve domain data products for reuse across analytics, semantic layers, ML, and AI applications.
Define and uphold data contracts including schemas, freshness, breaking-change policy, and responsibilities.
Design data products for AI/ML consumption with reproducibility and data quality controls.
Diagnose and resolve complex production and data quality issues and drive root cause resolution.
Use AI-assisted tooling to accelerate engineering while ensuring security, privacy, and quality.
Own the technical cost efficiency of data products including sizing and optimization.
Evaluate tools and platform capabilities within enterprise standards.