below marketmost similar roles pay hereabove market
This role pays less than
72%
of similar roles. Most pay
$143,750–$226,750
— the blue band above.
At the midpoint, this role pays about
$149k
versus about
$185k
for comparable roles.
Based on 240 similar postings.
Employer
About Abbott
Abbott Laboratories is a global healthcare company that manufactures and markets a broad and diversified line of health care products including diagnostics, medical devices, nutritionals, and branded generic pharmaceuticals. Industry: Healthcare & Medical Devices
Abbott currently has
217 open roles
on FindRole.
Listed pay typically runs
$99,300–$198,700
across 126 roles with salary data.
The Staff Data Engineer is a senior, hands-on technical contributor within the Enterprise Data domain. This role involves owning the technical design and complex implementation of data products, capabilities, and integrations. The engineer will build and evolve domain data products that are discoverable, governed, and observable for use in analytics, machine learning, and AI applications. Key responsibilities include defining data contracts, managing technical cost efficiency, and mentoring engineers. The position requires advanced SQL and Python skills, along with experience in relational, dimensional, and semi-structured data modeling. Candidates must be proficient with Databricks, Spark, and cloud infrastructure like AWS, Azure, or Google Cloud Platform. Technical requirements also include version control, CI/CD, Airflow, dbt, and Unity Catalog. The work involves handling protected health information under HIPAA and CLIA standards.
What does a Data Engineer earn?
Median $157350 from 201 postings across 60 companies.
Own technical design and hands-on delivery for complex data products, pipeline architectures, and integration patterns.
Produce design documentation and lead design reviews with domain and cross-domain peers.
Build and evolve data products to ensure they are documented, governed, observable, and supported for AI and ML applications.
Define and uphold data contracts including schemas, freshness expectations, and breaking-change policies.
Diagnose and resolve complex production issues and drive root cause resolution for data quality problems.
Manage technical cost efficiency by optimizing compute, warehouse sizing, and query performance.
Translate business requirements into technical solutions and communicate trade-offs to non-technical stakeholders.
Mentor engineers and provide code reviews to raise quality and consistency across the domain team.
What we're looking for
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 skills with experience in relational, dimensional, and semi-structured data modeling.
Experience designing and operating scalable distributed data systems on modern platforms like Databricks (including Spark) and cloud services like AWS, Azure, or GCP.
Experience with version control, automated testing, CI/CD, and orchestration/transformation tools like 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 translate business needs into technical solutions, mentor engineers, and raise technical quality across a team.
Experience with streaming and event-driven architectures such as Kafka (preferred).
Experience in regulated environments such as HIPAA, CLIA, SOX, FDA, ISO 13485, or IEC 62304 (preferred).
Experience with semantic layers, metric definitions, data products, or feature pipelines for ML and AI (preferred).
Experience with open data file formats such as Parquet, Avro, and Delta Lake (preferred).
Experience with REST API development and integration patterns (preferred).
Familiarity with business intelligence concepts and semantic consumption patterns for tools like Tableau (preferred).