3 days ago
Base Salary
$99k - $199k/yr
Responsibilities
- Own technical design and hands-on delivery for complex data products, pipelines, integrations, data models, and platform capabilities.
- Build and evolve governed, discoverable, observable, documented, and supportable data products for analytics, semantic layers, machine learning, and AI applications.
- Define data contracts covering schemas, freshness, availability, breaking changes, and producer and consumer responsibilities.
- Design data products for machine learning and AI consumption with reproducibility, lineage, timeliness, and data quality controls.
- Diagnose complex production and data quality issues and drive root-cause resolution.
- Optimize compute, warehouse, job, query, serverless, storage, and cost-to-serve decisions.
- Work with business stakeholders and partner organizations to translate needs into technical solutions and communicate tradeoffs.
- Set technical direction, lead design and code reviews, mentor engineers, and guide implementation without formal authority.
- Lead adoption and continuous improvement of testing, observability, CI/CD, and delivery practices.
- Ensure solutions meet quality, reliability, scalability, performance, security, privacy, access, lifecycle, and compliance requirements.
- Design handling of protected health information and other sensitive data, including access control, masking, de-identification, lineage, retention, and audit support.
- Serve as a subject matter expert during significant production incidents and support the company Quality Management System.
Requirements
- 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.
- Experience owning technical design and hands-on delivery of complex data products, pipelines, or platform capabilities through production support.
- Advanced SQL and Python skills, including relational, dimensional, and semi-structured data modeling.
- Experience designing and operating scalable distributed data systems on platforms such as Databricks and Spark, using AWS, Azure, or Google Cloud Platform.
- Experience with version control, automated testing, CI/CD practices, and orchestration or transformation tools such as Databricks Workflows, Delta Live Tables, Airflow, or dbt.
- Experience with governed data, cataloging, lineage, access control, and data quality capabilities such as Unity Catalog.
- Ability to work with business stakeholders, translate needs into technical solutions, mentor engineers, and improve technical quality across a team.
- Preferred experience with streaming and event-driven architectures such as Kafka.
- Preferred experience in regulated environments involving HIPAA, CLIA, SOX, FDA, ISO 13485, or IEC 62304, ideally in life sciences, diagnostics, or clinical laboratory data handling protected health information.
- Preferred experience with semantic layers, metric definitions, data products, or feature pipelines supporting analytics, machine learning, and AI use cases.
- Preferred experience with Parquet, Avro, Delta Lake, REST API development and integration patterns, and business intelligence tools such as Tableau.
Benefits
- The role is based in Madison, Wisconsin, with relocation assistance potentially available for qualified candidates.
- Standard work shift with approximately 5% travel.
- Continuous sitting and keyboard use are significant work activities for the position.
Tech Stack
Categories
Data Engineering
About Abbott
Abbott is a NYSE‑listed healthcare company that develops diagnostics, medical devices, nutrition products, and branded generic medicines for clinicians and consumers. Founded in 1888 and headquartered in Abbott Park, Illinois, it sells products in more than 160 countries, including the FreeStyle Libre continuous glucose monitoring system. Revenue comes primarily from product sales to hospitals, clinics, and retail channels.