Data Quality Engineer (Data QE)

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
  

Modern 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