Sr DQA-Databricks-Snowflake

Job Requirements

Virtusa is looking for a hands-on Mid-Level Data Quality Engineer (DQE) to join our Data Quality

Center of Excellence (CoE) delivery team for a premier healthcare payer client.

You will play a crucial role in operationalizing a "Shift-Left" data quality

strategy—building automated validation checks natively within Databricks and Snowflake to

catch data anomaliesHands-On DQxC Development Write Quality Code: Develop robust, modular data quality rules and validation scripts

using Python, PySpark, and SQL to profile and validate large volumes of healthcare

data.

Databricks & Snowflake Execution: Implement DQE frameworks within Azure

Databricks (processing Delta tables) and Snowflake to ensure data accuracy, completeness, and consistency.

Translate Rules to Code: Work closely with Data Stewards and the DQA Lead to

translate documented business data rules (e.g., valid ICD-10 codes, NPI formats) into

executable automated checks.

Pipeline Integration & Automation

Shift-Left Implementation: Integrate your automated data quality tests directly into

Azure DevOps CI/CD pipelines

Continuous Monitoring: Build programmatic alerts and log DQE results into centralized

observability dashboards so the business can monitor data health in real-time.

Leverage Gen AI: Actively utilize AI coding assistants to accelerate the writing of PySpark/SQL code, generate unit tests, and

document data quality scripts.

Technical Expertise

Experience: 3–6 years of hands-on experience in Data Engineering, Data Analytics, or

Software Quality Engineering with a heavy data focus.

Core Languages: Strong proficiency in Python and SQL. You must be comfortable

writing complex queries and modular Python scripts.

Data Platforms: Proven hands-on experience with Databricks

Code Management: Solid understanding of version control (Git) and experience

working within CI/CD workflows (Azure DevOps preferred).

Automation-First: A strong belief that data quality should be treated as a software

engineering discipline—automated, version-controlled, and tested.

Preferred Qualifications

Healthcare Exposure: Previous experience working with healthcare data and a basic understanding of PHI/PII handling requirements.

DQ Frameworks: Exposure to modern open-source or enterprise data quality

frameworks such as Great Expectations, dbt tests, Soda,.

Work Experience

7-10Years