Data Quality Engineer (Data QE)

Anblicks Inc.; Hyderabad; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Anblicks is seeking a Data Quality Engineer (Data QE) with 5+ years of experience to ensure data accuracy, integrity, and reliability across complex data pipelines using SQL, Python, and modern platforms like Databricks and Snowflake.

You will design and execute data quality tests, validate ETL/ELT processes, build test automation, and integrate tests into CI/CD workflows while collaborating with data engineering and AI teams.

Qualifications

• 5+ years of experience in data quality engineering or equivalent.
• Strong SQL and Python for data validation and automation.
• Experience with ETL/ELT testing and data warehousing concepts.

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.

Skills

SQL

Python

ETL/ELT testing

Data validation

Tools

Databricks

Snowflake

Delta Lake

Unity Catalog

Git

Jenkins

Azure DevOps

Airflow

Great Expectations

Soda

Deequ

Job description

Job Description: Data Quality Engineer (Data QE)

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.
• 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.
• 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.