Data Quality Engineer (Data QE)

Job Description: Data Quality Engineer (Data QE) Location: Hyderabad, India Experience: 5+ Years Company: Anblicks About Anblicks Anblicks is a Great Place to Work® Certified™ Data and AI company, helping enterprises accelerate digital transformation through modern data platforms, cloud engineering, analytics, and AI-driven solutions. Role Overview We are looking for a skilled Data Quality Engineer with strong expertise in SQL, Python, ETL/ELT testing, and data test automation. The candidate will ensure data accuracy, integrity, and reliability across complex data pipelines using modern platforms such as Databricks and Snowflake. Exposure to Generative AI, LLMs, RAG pipelines, and AI agents is an added advantage. Key Responsibilities Design and execute data quality testing strategies for end-to-end data pipelines. Perform ETL/ELT testing, source-to-target validation, data reconciliation, and business rule verification. Develop advanced SQL queries and Python scripts for data validation and test automation. Validate Databricks workflows, Delta tables, and Bronze, Silver, and Gold data layers. Test incremental loads, CDC, schema changes, data integrity, and failure-recovery scenarios. Build reusable, configuration-driven data quality automation frameworks. Integrate automated tests into CI/CD pipelines and workflow orchestration tools. Investigate data discrepancies, perform root cause analysis, and collaborate with data engineering teams. Leverage AI-assisted tools to improve test case generation, SQL development, and failure analysis. Required Technical Skills SQL & Python Strong SQL skills, including complex joins, CTEs, window functions, and aggregations. Proficiency in Python for data validation, reconciliation, and automation. Exposure to PySpark for large-scale data processing and validation. Data Quality & Engineering Strong understanding of ETL/ELT testing, data warehousing, data modelling, and data profiling. Experience with source-to-target validation, business rules, and data integrity checks. Knowledge of batch/streaming pipelines and formats such as JSON, CSV, and Parquet. Databricks & Cloud Platforms Hands-on experience with Databricks notebooks, Spark SQL, jobs, and workflows. Knowledge of Delta Lake, schema evolution, and Medallion Architecture. Exposure to Snowflake, Unity Catalog, and cloud data platforms is desirable. Automation & CI/CD Experience building reusable SQL/Python-based test automation frameworks. Knowledge of PyTest or similar testing frameworks. Experience with Git, Jenkins, Azure DevOps, or equivalent CI/CD tools. Familiarity with Databricks Workflows or Apache Airflow. AI & Emerging Technologies Understanding of Generative AI, LLMs, and AI-assisted testing. Exposure to RAG pipeline validation, including document quality, metadata, retrieval relevance, and response accuracy. Familiarity with LLM evaluation, AI agents, and responsible AI practices is an advantage. Good to Have Experience with Great Expectations, Soda, or Deequ. Knowledge of Kafka, data observability, and anomaly detection. Exposure to AWS, Azure, GCP, RAG evaluation tools, and data governance.