AI Data & Quality Analytics Tester(Bangalore only)

PwC; Bengaluru; On-site; INR 1,200,000 - 1,900,000; Full time

Job summary

PwC in Bengaluru seeks a data quality engineer to build automated validation for data pipelines and to strengthen data integrity across source-to-target processes. You will implement CI/CD quality gates, validate metrics, and collaborate with AI analytics teams to ensure reliable results.

The role emphasizes SQL, Python, Pandas/NumPy, and modern data tools, with a focus on testing strategies, telemetry-driven risk analysis, and clear communication of quality risks to stakeholders.

Qualifications

• Proficient in SQL and data validation workflows.
• Experience with Pandas/NumPy for data analysis.
• Familiarity with OpenAI/Azure OpenAI APIs and AI-assisted testing.
• Experience designing test strategies for data quality pipelines.

Responsibilities

• Perform data validation and reconciliation across source-to-target pipelines using SQL and automated checks
• Design and execute test strategies for data quality pipelines, including test data preparation, labeling verification, and schema validation
• Validate predictive quality metrics (trend forecasts, risk scoring, defect leakage signals) for correctness and stability
• Execute defect trend analysis and risk-based testing using telemetry and historical patterns
• Implement automated data validation in CI/CD pipelines and ensure continuous quality gates
• Conduct prompt testing and A/B comparisons for AI-assisted analytics interactions

Skills

Data validation

SQL proficiency

Python for data validation

A/B testing

Analytical reasoning

Communication of risks

Tools

SQL (Postgres/Snowflake)

Pandas / NumPy

Promptfoo

Test data profiling tools

OpenAI / Azure OpenAI APIs

Cursor AI / GitHub Copilot / Claude CLI

Job description

Key Responsibilities

• Perform data validation and reconciliation across source-to-target pipelines using SQL and automated checks
• Design and execute test strategies for data quality pipelines, including test data preparation, labeling verification, and schema validation
• Validate predictive quality metrics (trend forecasts, risk scoring, defect leakage signals) for correctness and stability
• Execute defect trend analysis and risk-based testing using telemetry and historical patterns
• Implement automated data validation in CI/CD pipelines and ensure continuous quality gates
• Conduct prompt testing and A/B comparisons for AI-assisted analytics interactions (where applicable)
• Leverage LLMs for: Intelligent test data generation
• Automated data labeling validation
• AI-assisted anomaly detection in data quality pipelines
• Produce actionable insights via statistical thinking and analytical reasoning; communicate quality risks clearly to stakeholders

Must Have Tools

• SQL (Postgres / Snowflake)
• Pandas / NumPy
• Python (data validation)
• Promptfoo (A/B prompt tests)
• Test data profiling tools
• OpenAI / Azure OpenAI APIs
• Cursor AI / GitHub Copilot / Claude CLI