The Role
Designs and executes data quality testing for end-to-end ETL/ELT pipelines. Responsibilities include source-to-target validation, reconciliation, business rule verification, SQL and Python automation, Databricks and Delta Lake testing, CI/CD integration, failure analysis, and data integrity validation. The role also evaluates emerging AI and RAG pipelines and collaborates with data engineering teams to resolve discrepancies.
Summary Generated by Built In
Job Description: Data Quality Engineer (Data QE)
Location: Hyderabad, India
Experience: 5+ Years
Company: Anblicks
About AnblicksAnblicks 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 OverviewWe 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.
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.
- 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.
- 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.
Skills Required
- 5+ years of professional experience
- Strong SQL skills, including complex joins, CTEs, window functions, and aggregations
- Proficiency in Python for data validation, reconciliation, and automation
- Experience with ETL/ELT testing, data warehousing, data modeling, and data profiling
- Experience with source-to-target validation, business rules, and data integrity checks
- Understanding of batch and streaming pipelines and JSON, CSV, and Parquet formats
- Hands-on experience with Databricks notebooks, Spark SQL, jobs, and workflows
- Knowledge of Delta Lake, schema evolution, and Medallion Architecture
- 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
- Understanding of Generative AI, LLMs, and AI-assisted testing
- Exposure to PySpark for large-scale data processing and validation
- Exposure to Snowflake, Unity Catalog, and cloud data platforms
- 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
- Exposure to RAG pipeline validation, LLM evaluation, AI agents, and responsible AI practices
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The Company
What We Do
Anblicks is a Cloud Data Analytics Company based out of Dallas, TX, with offices in USA, India, and Australia. Since 2004, Anblicks has been helping customers by bringing value to their data and implementing modern data architecture and advanced analytics solutions in the cloud.
