Role: Data Quality Engineer - ETL Testing | AI Validation

Location: Bangalore

Level – 7+ Years

Immediate Joiners Only :

Solid hands-on expertise in ETL Testing, Data Quality Testing, and Test Automation.

Good understanding of data validation, data integrity, source-to-target (STT) validation, and end-to-end ETL testing.

Preferred exposure to LLM and Machine Learning Model Validation from a QA perspective.

Candidates with strong relevant project experience in the above areas will be given preference.

Technical Requirements

Data Engineering Validation

Advanced SQL: Expertise in complex analytical queries, window functions, and validating Slowly Changing Dimensions (SCD).

Warehousing Fundamentals: Deep understanding of Fact vs Dimension validation and Source-to-Target (STT) reconciliation.

Pipeline Integrity: Proven experience with Incremental vs Full Load testing, row count reconciliation, and data completeness checks.

Data Quality (DQ): Ability to design automated DQ checks, data profiling, and validation of business-specific transformation rules.

API Testing: Proficient in validating RESTful APIs and alternative integration patterns.





LLM & Generative AI Validation:

Prompt Engineering: Validating system prompts, user prompts, and few-shot learning impacts on model behavior.

Hallucination & Bias Detection: Implementing techniques to identify factual inaccuracies and social biases in model responses.

Model Parameters: Testing the impact of Temperature, Top-P, and Context Window limits on output quality.

Agentic Testing: Validating multi-step reasoning, human-in-the-loop workflows, and stress testing autonomous AI agents.

Benchmarking: Evaluating model performance against Golden Datasets using metrics like BLEU, ROUGE, or semantic similarity.

Preferred

Qualifications

Experience: 6+ years in Data Quality, ETL Testing, or AI Assurance.

Tools: Familiarity with dbt, Great Expectations, or LLM evaluation frameworks (LangChain,N8N,).

Cloud Environments: Hands-on experience with modern data stacks on Azure.

Mindset: A "non-deterministic" testing approach—comfortable working with distributions and probabilistic outputs rather than just True/False outcomes.

📌 Data Quality Engineer - ETL Testing | AI Validation (Bengaluru)
🏢 Innova ESI
📍 Bengaluru