Pay
$127K–$190K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 2,133 postings in the last year. Stated ranges, not salaries paid.
Staff Data Engineer
United States > Madison : 1 Exact Lane
Live, first seen by us today- Pay
Pay amount needs review — pay source
The base pay for this position is $99,300.00 – $198,700.00In specific locations, the pay range may vary from the range posted. JOB FAMILY:
Read the full posting- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
What you’ll work on
Full postingOwn technical design and hands-on delivery for complex data products and pipelines
Define data contracts covering schemas, freshness, and breaking-change policy
Design data products for AI and machine learning with lineage and quality controls
Diagnose and resolve complex production and data quality issues at the root cause
Mentor engineers and lead code and design review across the domain team
From the employer’s posting
Own technical design and hands-on delivery for the most complex or highest-risk work across assigned data products
Define and uphold data contracts for the products the domain publishes, including schemas, freshness and availability expectations, breaking-change policy
Design data products for AI and machine learning consumption with reproducibility, lineage, timeliness, and defined data quality controls
Diagnose and resolve complex production and data quality issues, and drive root cause resolution
Provide code and design review that raises quality and consistency across the domain team. Mentor engineers
What you’ll bring
All qualificationsCore experience
- 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.
- 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.
Preferred experience
- 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 analytics, machine learning, and AI use cases in production.
- Experience with open data file and table formats such as Parquet, Avro, and Delta Lake.
Qualification wording
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.
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.
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 analytics, machine learning, and AI use cases in production.
Experience with open data file and table formats such as Parquet, Avro, and Delta Lake.
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Nov 4, 2026today
- Recorded sightings
- 2
- Last seen by us
- Nov 5, 2026
- Reposted
- once since Sep 23, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications. We count a repost when the same title and location comes back as a new listing within 60 days of the last one coming down. Some hiring systems expire listings on a schedule, so repost rates vary by system.
How this job compares
Time since we first saw it
Day 0
Early. Similar jobs usually come down between day 6 and day 54.
At Abbott Laboratories, jobs usually come down by about day 23.
From 11,035 similar postings that came down, and 87 of Abbott Laboratories’s. Coming down is not always a hire.
Abbott Laboratories is hiring
33 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Hiring at a steady pace
- Takes jobs down quickly
- 6% of open jobs were reposted
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
- AWS
- Azure
- Databricks
- dbt
- Delta Lake
- Kafka
- Spark
- Tableau
- Airflow
- GCP
Where the posting names them
- 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.
- 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 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.
- Experience with semantic layers, metric definitions, data products, or feature pipelines supporting analytics, machine learning, and AI use cases in production. - Experience with open data file and table formats such as Parquet, Avro, and Delta Lake. - Experience with REST API development and integration patterns.
Preferred Qualifications - 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 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. The base pay for this position is
Your next step
Complete your application on abbott.wd5.myworkdayjobs.com. The employer’s form will show what is required.
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- Your application status lives in that employer’s Candidate Home, under the same sign-in.
Already applied? Track this application
Has Abbott Laboratories posted this job before?
Yes. We’ve followed one other Staff Data Engineer job there since September 2026. It is still up.
About Abbott Laboratories
- People
- About 115,000 in 2025, up 1% from the year before.Annual report (SEC Form 10-K)
- Company
- Listed as ABT.
- H-1B visas
- 30 approved for new hires in fiscal 2025.US Citizenship and Immigration Services
Demand for Data Engineer roles
New Data Engineer postings at the employers we follow are up 8% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
US government figures for database architects
- Median yearly pay, May 2025
- $139,500
- Expected change, 2025 to 2035
- 9.4% more jobsagainst 3.5% for all jobs
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
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
Include, but are not limited to, the following:
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, retention, and audit support consistent with HIPAA, CLIA, and enterprise privacy standards.
- Serve as domain subject matter expert for significant production incidents affecting domain systems.
- 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.
- Ability to act with an inclusion mindset and model these behaviors for the organization.
Minimum 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.
Preferred Qualifications
- 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 analytics, machine learning, and AI use cases in production.
- 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.
The base pay for this position is
$99,300.00 – $198,700.00In specific locations, the pay range may vary from the range posted.
JOB FAMILY:
Product Development
DIVISION:
ONCO Cancer Diagnostics
LOCATION:
United States > Madison : 1 Exact Lane
ADDITIONAL LOCATIONS:
WORK SHIFT:
Standard
TRAVEL:
Yes, 10 % of the Time
MEDICAL SURVEILLANCE:
No
SIGNIFICANT WORK ACTIVITIES:
Continuous sitting for prolonged periods (more than 2 consecutive hours in an 8 hour day), Keyboard use (greater or equal to 50% of the workday)Abbott is an Equal Opportunity Employer of Minorities/Women/Individuals with Disabilities/Protected Veterans.
EEO is the Law link - English: http://webstorage.abbott.com/common/External/EEO_English.pdf
EEO is the Law link - Espanol: http://webstorage.abbott.com/common/External/EEO_Spanish.pdf
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
The base pay for this position is $99,300.00 – $198,700.00In specific locations, the pay range may vary from the range posted. JOB FAMILY:
- Location & working pattern
United States > Madison : 1 Exact Lane
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.