About this role
Data professional with experience in building and validating end-to-end data pipelines across Azure Data Factory, Databricks, and ADLS. Skilled in data quality assurance, automation using Python and PySpark, and integrating tests into CI/CD workflows. Adept at large-scale data validation, monitoring data health, and ensuring reliable batch and streaming processes, with additional expertise in developing scalable BI dashboards and reporting solutions.
• Design and implement end-to-end data validation strategies for pipelines built on Azure Data Factory, Databricks (Delta Lake), and ADLS • Perform source-to-target reconciliation across ingestion, transformation, and consumption layers • Implement robust checks for Data Quality Dimensions • Build scalable data testing frameworks using python, pyspark and good knowledge of automation tools like playwright & pyspark based automation testing. • Integrate automated data tests into CI/CD pipelines (Azure DevOps / GitHub Actions) • Implement data quality-as-code practices using tools like Soda, dbt etc. • Validate Spark transformations, Delta Lake tables, and streaming pipelines • Perform large-scale data validation using PySpark and SQL • Optimize validation logic for high-volume datasets • Ensure correctness of: Batch and streaming jobs, Incremental loads (CDC pipelines), Slowly changing dimensions (SCD) • Validate data movement, orchestration workflows, and failure handling in Azure Data factory & Azure Databricks services • Define and implement data quality SLAs and KPIs • Build dashboards to track data health and pipeline reliability • Proactively identify anomalies and data drifts • Implement alerting mechanisms for data failures • Design, develop, and optimize interactive dashboards and reports using BI tools (e.g., Power BI, Tableau, Looker). • Translate business requirements into technical reporting solutions. • Identify bottlenecks and improve report/query performance. • Implement best practices for dashboard design and scalability. • Establish BI standards, frameworks, and best practices.
Required Qualifications
• 6-8 years experience in enterprise data modeling and data architecture roles. • Strong hands-on experience with Azure Data Factory & Pyspark in large-scale environments is preferred. • Experience with Databricks (Delta Lake, Spark, Unity Catalog). • Advanced SQL and strong understanding of pyspark scripts & using pyspark for Data validation. • Experience integrating major enterprise applications (ERP, CRM, OMS, MDM, AR systems). • Strong understanding of data governance, data quality, metadata, and lineage. • Excellent communication skills across business and technical audiences.
Core Technology Stack
• Analytics Platform: Databricks (Delta Lake, Unity Catalog), Microsoft PowerBI • Languages: SQL, Spark SQL, Python / PySpark • Governance/Data Quality: Unity Catalog/Informatica DG/DQ
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