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This role is a highly technical Data Quality Engineer supporting enterprise data validation and quality engineer efforts across the Azure/Databricks platform. As a senior level developer, this role requires expertise in data testing, automation, distributed systems, and modern data architectures. Other key responsibilities include:

- Design and implement end-to-end data validation strategies for pipelines built on Azure Data Factory, Databricks (Delta Lake), and ADLS
- Build and perform large-scale data validation using Python and Spark-based validation frameworks
- Validate data movement, orchestration workflows, and failure handling
- Define and implement data quality SLAs and KPIs along with tracking data/pipeline health
- Drive best practices for data quality and testing standards

Must Haves

- 6-8+ years' experience in data quality engineer, SDET, data engineering testing, etc.




- Strong hands-on experience with Azure Data Factory, Databricks (PySpark), and SQL for data quality validation
- Proven experience building automated data quality frameworks from scratch
- Solid understanding of data governance, lineage, and metadata
- Experience integrating with enterprise systems (ERP, CRM, MDM, etc.)

Plusses

- Data observability platforms (Monte Carlo, Soda, Datadog)
- Experience building CI/CD data testing pipelines (Azure DevOps / GitHub Actions)
- Streaming Technologies (Kafka, Event Hub)
- Hands-on experience with dbt (Data Built Tool)
- Azure Data Engineer (DP-203) Certification or Databricks Certified Professional
- Experience within supply chain or large enterprise data environments

📌 Data Quality Engineer (Bengaluru)
🏢 Insight Global
📍 Bengaluru