ETL Data Engineer & Analytics Specialist

Quest Oracle Community; Town of Texas (WI); Hybrid; USD 44,000 - 102,000; Full time

Job summary

CVS Health is seeking a data engineer to build ETL workflows using Dataiku and Python, transforming and populating data. You will work with product managers, clinical support, and designers to capture requirements and document solutions.

You’ll help test, troubleshoot, and resolve data issues, write queries, and analyze datasets to support client retention. You’ll participate in knowledge sharing and team initiatives to advance data engineering goals.

Qualifications

• Bachelor's Degree or equivalent years of experience.
• 2+ years experience in ETL work with Dataiku or Alteryx.
• 2+ years' experience with Python.
• 2+ years' experience in data engineering.
• Strong problem solving and decision-making skills.
• Ability to work independently and in a team.
• Excellent communication skills.
• Ability to work with large data sets from multiple sources.

Responsibilities

• Build ETL workflows using Dataiku and Python to receive, manipulate and populate data.
• Collaborate with cross-functional teams to gather and document product requirements.
• Support testing and troubleshooting and document work.
• Assist in retaining clients by researching and resolving data issues with queries and analysis.
• Investigate data sets to answer internal questions.
• Contribute to knowledge management to share best practices.

Skills

SQL

Python

Dataiku

Alteryx

Data engineering

Problem solving

Communication

Large datasets

Education

Bachelor's degree or equivalent

Tools

Dataiku

Alteryx

Azure DevOps

Job description

CVS Health is seeking a data engineer to build ETL workflows using Dataiku and Python, transforming and populating data. You will work with product managers, clinical support, and designers to capture requirements and document solutions.

You’ll help test, troubleshoot, and resolve data issues, write queries, and analyze datasets to support client retention. You’ll participate in knowledge sharing and team initiatives to advance data engineering goals.