Data Quality Engineer (Databricks & dbt)

Cognizant; Princeton (NJ); Remote; USD 70,000 - 117,000; Full time

Benefits offered by this job

Medical Insurance

Dental Insurance

Vision Insurance

Life Insurance

401(k) plan

Paid holidays

Paid Time Off

Disability Insurance

Parental Leave

Employee Stock Purchase Plan

Job summary

Cognizant Technology Solutions Corporation is seeking a Data Quality Engineer to design, implement, and automate data quality tests across Databricks and dbt environments. You will integrate quality checks into CI/CD pipelines via Azure DevOps and collaborate with data engineers, analysts, and product owners to ensure data accuracy and governance.

Responsibilities include validating pipelines, defining quality metrics, and maintaining test documentation, with a focus on source-to-target

Qualifications

• 5+ years in QA, data testing, or data engineering roles with a focus on data validation.
• Hands-on experience with dbt (Core or Cloud): tests, macros, models, documentation.
• Strong Python skills (pandas, PySpark, pytest) for test automation.
• Practical experience with Databricks: notebooks, jobs, Delta tables, Unity Catalog.
• Experience building and maintaining Azure Pipelines (Azure DevOps) for CI/CD.
• Solid understanding of data quality concepts, dimensions, and frameworks.
• Experience with Git and version-controlled development workflows.
• Experience in using GitHub copilot

Responsibilities

• Design, build, and maintain automated data quality tests in dbt (schema tests, custom tests).
• Develop Python-based validation frameworks and scripts for data reconciliation and tests.
• Validate data pipelines and transformations in Databricks (PySpark/SQL, Delta Lake).
• Integrate data quality gates into Azure Pipelines for CI/CD.
• Define, monitor, and report on data quality metrics and set up anomaly alerts.
• Triage and root-cause data defects with engineers and analysts, tracking to resolution.
• Write and maintain test plans, test cases, and data quality documentation.
• Contribute to data governance and quality standards, including data contracts and lineage.

Skills

QA experience

dbt tests

Python

Databricks

Azure DevOps

Data quality concepts

Git

GitHub Copilot

Tools

dbt Core/Cloud

Azure Pipelines

Databricks

PySpark

Delta Lake

Unity Catalog

Job description

Data Quality Engineer (Databricks & dbt)

About the role

We are seeking a Data Quality Engineer to ensure the accuracy, consistency, and reliability of enterprise data platforms. The ideal candidate will design and automate data quality testing frameworks using dbt, Python, and Databricks while integrating quality controls into CI/CD pipelines through Azure DevOps. This role will partner closely with data engineers, analysts, and business stakeholders to identify and resolve data issues, monitor key quality metrics, and drive continuous improvements across the data ecosystem. Strong experience in data validation, test automation, and modern cloud-based data platforms is essential.

In this role, you will:

• esign, build, and maintain automated data quality tests in dbt (schema tests, custom generic tests, singular tests, source freshness checks).
• Develop Python-based validation frameworks and scripts for data reconciliation, schema drift detection, and regression testing.
• Validate data pipelines and transformations in Databricks (PySpark/SQL, Delta Lake), covering source-to-target mapping, row counts, aggregations, and business rules.
• Integrate data quality gates into Azure Pipelines, so builds and releases are blocked when tests fail.
• Define, monitor, and report on data quality metrics (completeness, accuracy, consistency, timeliness, uniqueness), and set up alerting for anomalies.
• Triage and root-cause data defects with data engineers, analysts, and product owners, and track them through to resolution.
• Write and maintain test plans, test cases, and data quality documentation.
• Contribute to data governance and quality standards, including data contracts and lineage awareness.

We strive to provide flexibility wherever possible. Based on this role's business requirements, this is a remote position open to qualified applicants in the USA. Regardless of your working arrangement, we are here to support a healthy work-life balance through our various wellbeing programs.

The working arrangements for this role are accurate as of the date of posting. This may change based on the project you're engaged in, as well as business and client requirements. Rest assured; we will always be clear about role expectations.

What you need to have to be considered

• 5+ years in QA, data testing, or data engineering roles with a focus on data validation.
• Hands-on experience with dbt (Core or Cloud): tests, macros, models, documentation.
• Strong Python skills (pandas, PySpark, pytest) for test automation.
• Practical experience with Databricks: notebooks, jobs, Delta tables, Unity Catalog.
• Experience building and maintaining Azure Pipelines (Azure DevOps) for CI/CD.
• Solid understanding of data quality concepts, dimensions, and frameworks.
• Experience with Git and version-controlled development workflows.
• Experience in using GitHub copilot

Salary and Other Compensation

Applications will be accepted until December 3, 2026.

The annual salary for this position is between $70,000 - $117,000 depending on experience and other qualifications of the successful candidate.
This position is also eligible for Cognizant's discretionary annual incentive program, based on performance and subject to the terms of Cognizant's applicable plans.

Benefits: Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:

• Medical/Dental/Vision/Life Insurance
• Paid holidays plus Paid Time Off
• 401(k) plan and contributions
• Long-term/Short-term Disability
• Paid Parental Leave
• Employee Stock Purchase Plan

Disclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.