Data Engineer, Digital Solutions Finance

Cardinal Health

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Work type
On-site
Location
—
Employment
Full-time
Posted
62 days ago
Freshness
Confirmed live today

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Salary context

How this pay compares to similar roles

Similar $169k
$117k $221k
below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $126,800–$211,200.

Based on 240 similar postings.

Employer

About Cardinal Health

Cardinal Health is a global healthcare services and products company specializing in pharmaceutical distribution, medical products, and supply chain solutions for healthcare providers and pharmacies. Industry: Healthcare Distribution & Services

Cardinal Health currently has 65 open roles on FindRole.

Listed pay typically runs $96,300–$137,600 across 27 roles with salary data.

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At a glance

TL;DR · Data Engineer, Digital Solutions Finance

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Data Engineer, Digital Solutions Finance joins the Data & Analytics Function to support the analytical and operational requirements of Finance stakeholders. The role involves designing, building, and maintaining robust data pipelines and architecture to ensure raw data is usable and accessible. Key responsibilities include ingesting, processing, and transforming large-scale datasets, implementing data quality checks, and migrating legacy workflows to modern cloud platforms. The position requires technical proficiency in SQL and programming languages like Python, Scala, or Java. Candidates must utilize Databricks and Google Cloud Platform services, specifically BigQuery, Cloud Storage, and Dataflow. The work focuses on data modeling, ETL/ELT processes, and performance tuning to optimize processing times and minimize infrastructure costs while providing high-quality datasets for data scientists and business analysts.

What does a Data Engineer earn?

Median $159000 from 203 postings across 58 companies.

See salary data

Skills

Databricks GCP BigQuery Python SQL Spark Scala Cloud Storage Dataflow ETL ELT Data Modeling Java IAM

What you'll do

  • Design, build, and optimize scalable data pipelines using Databricks and GCP services.
  • Ingest, process, and transform large-scale datasets to support Finance stakeholders.
  • Implement data quality checks, monitoring, and alerting mechanisms to ensure data reliability.
  • Perform performance tuning of key processes to optimize processing times and minimize cloud costs.
  • Migrate legacy data workflows to modern cloud platforms.
  • Participate in code reviews and technical design discussions to establish engineering best practices.

What we're looking for

  • 4-8 years of experience (preferred)
  • Bachelor’s degree in Computer Science, Information Technology, or a related field, or equivalent practical experience.
  • Strong proficiency in SQL and at least one programming language such as Python, Scala, or Java.
  • Hands-on experience developing data solutions using Databricks (preferred)
  • Solid experience working within the Google Cloud Platform ecosystem, particularly with BigQuery, Cloud Storage, and IAM.
  • Experience with data modeling, ETL/ELT processes, and data warehouse concepts.
  • Proven ability to work independently, manage multiple priorities, and deliver projects of varying difficulty.

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