Data Quality Engineer

dnA Consulting; Mysuru; On-site; INR 1,400,000 - 2,300,000; Full time

Job summary

dnA Consulting is seeking a Data QA professional to own data quality validation and business-logic checks across data pipelines. You will design test strategies, perform source-to-target reconciliations, and build reusable validation scripts on Azure Databricks.

The role requires strong SQL skills and experience validating Power BI reports against underlying data. You will collaborate with Finance, Pricing, and Operations stakeholders, document test evidence, and track defects in Azure DevOps to

Qualifications

• 5-8 years of experience in data QA, ETL/ELT testing, or data-focused business analysis.
• Strong SQL skills for complex validation queries and data profiling.
• Experience testing data pipelines on a cloud data platform, preferably Azure Databricks.
• Ability to validate business logic and calculations, not only record counts.
• Experience with writing requirements, mappings, and acceptance criteria.

Responsibilities

• Design test strategies and cases for data pipelines, including data movement, schema, and business logic.
• Perform source-to-target reconciliations across systems, lakehouse, and reports.
• Validate outputs are correct and non-empty before release with dependency checks.
• Build reusable SQL- and Python-based validation scripts and automate data quality checks.
• Log, triage, and track defects in Azure DevOps through resolution.
• Document test evidence for releases for auditability.
• Coordinate UAT with business users and capture sign-off.

Skills

SQL

Data validation

Data quality testing

Communication

Tools

Azure Databricks

Delta tables

Azure DevOps

Power BI

Job description

Role & responsibilities

Data QA & Business-Logic Validation (Primary 70%)

• Design test strategies and test cases for data pipelines, covering both data movement (completeness, counts, schema) and business logic (calculations, transformations, joins, aggregations, business rules).


• Perform source-to-target reconciliation across source systems, the Databricks lakehouse, and Power BI reports.


• Validate that outputs are correct and non-empty before release, including dependency and sequencing checks across pipelines.


• Build reusable SQL- and Python-based validation scripts and contribute to automated data quality checks in pipelines.


• Document test evidence for every change so that releases can be reviewed and audited.


• Log, triage, and track defects in Azure DevOps through to resolution, and verify fixes.

Business Analysis (30%)

• Gather and document requirements with business stakeholders in Finance, Pricing, and Operations, including acceptance criteria.


• Write clear source-to-target mappings and business rule definitions that engineers can build from.


• Coordinate User Acceptance Testing (UAT) with business users and capture sign-off.

Data Analysis

• Profile data to identify quality issues, anomalies, and gaps before they reach the business.


• Investigate data discrepancies reported by users and explain root causes in plain language.

Preferred candidate profile

• 5-8 years in data QA, ETL / ELT testing, or data-focused business analysis.


• Strong SQL skills for complex validation queries, reconciliation, and data profiling.


• Hands-on experience testing data pipelines on a cloud data platform, preferably Azure Databricks (Databricks SQL, Delta tables).


• Proven ability to validate business logic and calculations, not only record counts.


• Experience writing requirements, source-to-target mappings, and acceptance criteria.


• Experience with Azure DevOps (or Jira) for test cases, defects, and work tracking.


• Experience validating Power BI reports against underlying data.


• Clear communicator who can explain data issues to non-technical business users.