Job Summary

We are seeking an experienced Data Quality Engineer (Data
QE) to ensure the quality, accuracy, reliability, and integrity of data
across enterprise data platforms. The ideal candidate will have strong
experience in Data Warehousing, Medallion Architecture, ETL/ELT testing,
Data Governance, Data Quality frameworks, Data Lineage, and test automation.
This role will be responsible for validating data pipelines from multiple
source systems such as CDP, GA4, BigQuery, and Azure Blob Storage into Snowflake,
and ensuring accurate reporting in Power BI.

Required Experience

- __
years of experience in Data Quality Engineering, Data Warehouse Testing,
or Data Validation.

- Strong
hands-on experience with PostgreSQL, Microsoft SQL Server, Snowflake and
BigQuery.

- Experience
validating enterprise Data Warehouse solutions and Medallion Architecture
implementations.

- Proven
experience performing Source-to-Target validation from CDP, GA4, BigQuery,
Blob Storage, PostgreSQL, and MSSQL into Snowflake.

- Experience
in validating Power BI reports, dashboards, semantic models, and KPIs.

- Strong
understanding of ETL/ELT data processing, data transformations, and data
lineage.

- Experience
working with Data Governance, Data Quality, Metadata Management, Data
Lineage, Data Dictionary, and Data Catalog solutions.

- Experience
with Data Quality tools such as Soda or equivalent.

- Experience
designing, executing, and automating test cases for large-scale data
platforms using SQL, Python, and PySpark.

- Experience
integrating automated testing into CI/CD pipelines using Azure DevOps or
GitHub Actions.

Key Responsibilities

Data Validation & Testing

- Perform
end-to-end data validation across the data lifecycle, including ingestion,
transformation, storage, and reporting.

- Validate
data movement from source systems including:

- Customer
Data Platforms (CDP)

- Google
Analytics 4 (GA4)

- Google
BigQuery

- Azure
Blob Storage

- PostgreSQL

- MSSQL

- Snowflake

- Perform




comprehensive Source-to-Target (S2T) data validation between source
systems and Snowflake.

- Validate
data accuracy, completeness, consistency, uniqueness, and timeliness
across datasets.

- Execute
data reconciliation and data profiling activities.

- Conduct
source-to-target validation across databases, data lakes, and data
warehouses.

- Validate
schema changes, constraints, indexes, stored procedures, functions,
triggers, and views.

Medallion Architecture Validation

- Validate
data across Bronze, Silver, and Gold layers within the Medallion
Architecture.

- Verify
data transformations, cleansing, aggregations, and business rules between
layers.

- Ensure
data quality controls are enforced throughout the Medallion framework.

ETL/ELT Testing

- Test
and validate ETL/ELT pipelines and data transformations.

- Validate
business mappings, transformation logic, and data lineage.

- Verify
incremental and full-load processes.

- Perform
regression, integration, functional, and end-to-end testing of data
pipelines.

- Identify
and troubleshoot data discrepancies across source and target systems.

Snowflake Data Validation

- Validate
data ingestion, transformations, and storage within Snowflake.

- Perform
large-scale data validation using SQL and automation scripts.

- Validate
Snowflake views, tables, materialized views, stored procedures, and data
sharing mechanisms.

Power BI Testing

- Validate
datasets, data models, measures, KPIs, and dashboards.

- Ensure
Power BI reports accurately reflect Snowflake data.

- Verify
report-level calculations, filters, row-level security, and business
metrics.

- Perform




end-to-end testing from source systems through Snowflake into Power BI.

Data Governance & Data Management

- Validate
implementation of Data Governance policies and standards.

- Ensure
compliance with enterprise data quality and governance requirements.

- Validate
and maintain:

- Data
Lineage

- Data
Dictionary

- Metadata
Management

- Business
Glossary

- Data
Catalogs

- Collaborate
with Data Governance and Business teams to improve data quality processes.

Data Quality Frameworks

- Implement
and execute data quality checks and monitoring solutions.

- Define
and monitor Data Quality KPIs and metrics.

- Develop
and automate validation rules for:

- Completeness

- Accuracy

- Consistency

- Uniqueness

- Validity

- Timeliness

- Support
root cause analysis and remediation of data quality issues.

Test Automation

- Develop
automated data validation frameworks and reusable test suites.

- Automate
source-to-target reconciliation and regression testing.

- Integrate
automated data quality tests into CI/CD pipelines.

- Build
automated validation using SQL, Python, PySpark, and Data Quality tools.

Required Technical Skills

Databases & Data Warehousing

- PostgreSQL

- Microsoft
SQL Server (MSSQL)

- Snowflake

- BigQuery

- Data
Warehouse Testing

- Data
Modeling Concepts

- Star
Schema & Snowflake Schema

- Slowly
Changing Dimensions (SCD)

- Fact
& Dimension Validation

- CDC
Validation

- Data
Migration Testing

SQL Expertise

- Advanced
SQL Query Writing

- Complex
Joins

- Window
Functions

- Aggregations

- Query
Optimization

- Data
Reconciliation Queries

- Data
Profiling and Data Analysis

Up-to-date Data Platform Validation

- Medallion
Architecture Validation (Bronze, Silver, Gold)

- Source-to-Target
Testing

- ETL/ELT
Testing

- Data
Lake & Data Warehouse Validation

- Power
BI Report & Dashboard Validation

📌 Data Quality Engineer (Data QE) (Hyderabad)
🏢 Recognized
📍 Hyderabad