ETL Test Architect - Data Warehouse Testing

Job Description :

ETL Test Architect Data Validation, BI Validation & DataOps Suite

Datagaps is looking for a hands-on ETL Test Architect to guide the evolution of DataOps Suite for real-world ETL/ELT validation, BI report validation, data quality, observability, catalog, and test data management use cases.

Role Purpose :

This role requires a senior practitioner who has hands-on experience in complex ETL/ELT, migration, modernization, and BI validation projects and can translate practical validation challenges into product requirements, reusable accelerators, and customer-ready solution patterns.

Key Responsibilities :

• Architect end-to-end validation strategies for ETL/ELT, data warehouse, data lake, cloud migration, modernization, and BI analytics programs.

• Identify corner cases, failure patterns, and validation gaps across pipelines, sources, targets, semantic layers, dashboards, and production data flows.

• Define validation patterns for source-to-target comparison, transformation logic, reconciliation, metadata, schema drift, data quality, observability, and BI report accuracy.

• Provide product guidance for modern platforms such as Databricks, Snowflake, Microsoft Fabric, Azure Synapse, BigQuery, Redshift, and similar ecosystems.

• Work with product, engineering, QA, solution engineering, sales, customer success, and implementation teams to convert field problems into scalable DataOps Suite capabilities.

• Create validation playbooks, reusable templates, solution accelerators, demo scenarios, and customer-facing best practices.

• Support discovery sessions, POCs, solution design workshops, RFP responses, demos, and implementation reviews.

Required Experience and Expertise:

• 10+ years of hands-on experience in ETL/ELT testing, data warehouse testing, data migration testing, BI validation, or enterprise data validation programs.

• Strong experience with complex migration and modernization projects across legacy systems, cloud data warehouses, data lakes, lakehouses, modern analytics platforms, and BI platforms.

• Deep understanding of source-to-target testing, transformation validation, reconciliation, aggregation checks, duplicate/null checks, control totals, data completeness, data consistency, and regression testing.

• Practical BI validation experience including report-to-database validation, dashboard regression, visual validation, filter/slicer validation, security validation, performance validation, and BI migration testing.

• Strong knowledge of source- and target-specific validation challenges across databases, files, APIs, cloud storage, SaaS/ERP/CRM systems, streaming feeds, and semi-structured data formats such as JSON, XML, and Parquet.