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# Senior Data Quality Engineer - INTL - India

- **Company:** Insight Global
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Microsoft Azure, Big Data, Data Architecture, Data Validation, Information Engineering, Data Governance, Extract Transform Load (ETL), Database Testing, Distributed Systems, Github, Python (Programming Language), Metadata, DataOps, SQL Databases, Systems Integration, Technical Data Management Systems, Datadog, Enterprise Software Applications, Azure Data Factory, Apache Spark, Data Lakes, Pyspark, Apache Kafka, Video Streaming, SDET, Databricks
- **Published:** September 2, 2026
- **Apply:** https://www.juju.com/job/00000000gqdgyh

## About the Role

* 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

 * Strong understanding of data governance, lineage, and metadata

Experience integrating with enterprise systems (ERP, CRM, MDM, etc. * 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

## Description

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


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