AI Data & Quality Analytics Tester(Bangalore only)

PwC; Bengaluru; On-site; INR 900,000 - 1,300,000; Full time

Job summary

PwC in Bengaluru seeks a data validation and quality assurance professional to validate data pipelines end-to-end, design test strategies for AI-enabled analytics, and monitor predictive quality metrics. You will implement automated checks in CI/CD, perform prompt testing, and collaborate with stakeholders to communicate risks and insights.

Proficiency in SQL, Python, and data tools is essential. The role emphasizes automated validation, data labeling checks, and anomaly detection to ensure data

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

Tools

SQL

Pandas

NumPy

Python

Promptfoo

Test data profiling

OpenAI APIs

Azure OpenAI

Cursor AI

GitHub Copilot

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