This role pays less than
77%
of similar roles. Most pay
$157,033–$219,750
— the shaded band above.
At the midpoint, this role pays about
$149k
versus about
$188k
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
85 open roles
on FindRole.
Listed pay typically runs
$99,300–$198,700
across 85 roles with salary data.
Staff Data Engineer serves as a senior, hands-on technical contributor within an Enterprise Data domain, owning complex technical designs and implementations for data products, capabilities, and integrations. The role involves building and evolving data products that are governed, observable, and supported for use in analytics, machine learning, and AI applications. Key responsibilities include defining data contracts, managing cost efficiency, mentoring engineers, and collaborating with business stakeholders to translate requirements into technical solutions. The position requires advanced SQL and Python skills, along with experience in relational, dimensional, and semi-structured data modeling. Technical requirements include working with Databricks, Spark, and cloud platforms like AWS, Azure, or Google Cloud Platform. Candidates should be proficient with tools such as Airflow, dbt, Delta Live Tables, Unity Catalog, and Kafka while navigating complex regulatory environments involving protected health information.
What does a Data Engineer earn?
Median $162000 from 205 postings across 57 companies.
Own technical design and hands-on delivery for complex data products, pipeline architectures, and integration patterns.
Produce detailed design documentation to ensure technical decisions are understood and reproducible by other teams.
Build and maintain governed data products that support analytics, machine learning, and AI applications.
Define and enforce data contracts including schemas, freshness, availability, and quality controls.
Optimize the technical cost efficiency of data products regarding compute, storage, and query performance.
Translate business requirements into technical solutions while communicating trade-offs to non-technical stakeholders.
Mentor engineers and perform code reviews to ensure high standards of quality and consistency across the team.
Ensure all data systems comply with security, privacy, and regulatory standards such as HIPAA and CLIA.
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) using AWS, Azure, or Google Cloud Platform.
Experience with version control, automated testing, CI/CD, and orchestration tools such as Databricks Workflows, Delta Live Tables, Airflow, or dbt.
Experience delivering governed data using catalog, lineage, access control, and quality capabilities like Unity Catalog.
Demonstrated ability to work with business stakeholders, mentor engineers, and translate requirements into technical solutions.
Experience with streaming/event-driven architectures (Kafka) (preferred).
Experience in regulated environments such as HIPAA, CLIA, SOX, FDA, ISO 13485, or IEC 62304 (preferred).
Experience with semantic layers, metric definitions, and feature pipelines for AI/ML use cases (preferred).
Experience with open data formats like Parquet, Avro, and Delta Lake (preferred).
Experience with REST API development and integration patterns (preferred).
Familiarity with business intelligence concepts and how platform design affects tools like Tableau (preferred).
Databricks
Delta Lake
Unity Catalog
AWS
Python
Java
SQL
NoSQL
Data Engineering
Data Modeling
Machine Learning
Data Governance
Fivetran
HVR
Vault
Data Observability