Lead Azure Data Engineer - Databricks & Payer Analytics

Cognizant; Blaine (MN); Hybrid; USD 100,000 - 130,000; Full time

Benefits offered by this job

Medical/Dental/Vision/Life Insurance

Paid holidays + PTO

401(k) plan and contributions

Long-term/Short-term Disability

Employee Stock Purchase Plan

Job summary

Cognizant is seeking a Sr Azure Data Engineer to lead enterprise-scale data engineering initiatives using Azure Databricks. The role focuses on designing, governing, and optimizing analytics solutions for payer domain stakeholders.

You will drive end-to-end Databricks workflows, collaborate with data architects and business analysts, and mentor teams in modern data engineering practices to improve data quality and operational efficiency.

Qualifications

• Twelve to fourteen years of progressive experience in data engineering or analytics.
• Advanced proficiency with Databricks notebooks, jobs, clusters and Delta Lake.
• Strong Python and SQL skills for data modeling and pipelines.
• Familiarity with payer or healthcare data domains and regulatory contexts.

Responsibilities

• Design scalable Databricks-based data pipelines for large structured and unstructured data.
• Lead end-to-end Databricks workflows with notebooks, jobs, and clusters.
• Collaborate with architects, product owners and analysts to translate needs into solutions.
• Optimize runtime configurations, storage formats and query patterns to reduce costs.
• Enforce coding standards, testing, and version control for data assets.
• Mentor teams on Databricks features and modern data engineering practices.

Skills

Azure Databricks

Python

SQL

Data governance

Leadership

Tools

Delta Lake

Databricks Notebooks/Jobs

Spark

Job description

Cognizant is seeking a Sr Azure Data Engineer to lead enterprise-scale data engineering initiatives using Azure Databricks. The role focuses on designing, governing, and optimizing analytics solutions for payer domain stakeholders.

You will drive end-to-end Databricks workflows, collaborate with data architects and business analysts, and mentor teams in modern data engineering practices to improve data quality and operational efficiency.