Data / AI QE Lead -- Retail eCommerce

Data Quality Engineering

• Define and own the QE strategy for data assets (customer, product, inventory, transaction, and behavioral event data).
• Design and implement data validation frameworks covering completeness, accuracy, consistency, timeliness, and referential integrity.
• Lead testing of ETL/ELT pipelines, data lake and warehouse layers, and real-time streaming pipelines.
• Establish data contract testing practices between producing and consuming systems.
• Build automated data quality monitors and alerting that operate continuously in production environments.
• Partner with data governance and data stewardship teams to align QE standards with enterprise data policies.

AI / ML Model Quality & Validation

• Lead quality validation for ML models powering eCommerce capabilities (product recommendations, personalized search, dynamic pricing, demand forecasting, propensity models, and generative AI).
• Define model evaluation frameworks including offline metrics and online business metrics (CTR, conversion rate, AOV, revenue lift).
• Design and execute A/B and shadow testing strategies to validate model performance before and during production rollout.
• Assess and test for model fairness, bias, and regulatory compliance across customer segments and product categories.
• Validate model monitoring and drift detection systems to ensure production models remain within acceptable performance thresholds.

eCommerce Platform Integration Testing

• Drive end-to-end quality of data flows from customer interaction events through to AI feature delivery on site, app, and email channels.
• Test integrations between the eCommerce platform and downstream data consumers (CDP, CRM, marketing automation, analytics tools).
• Validate real-time personalization pipelines for homepage, PDP, cart, and post-purchase experiences.
• Ensure data quality for key eCommerce events (product views, add-to-cart, checkout, order confirmation, returns, and search queries).
• Test search and browse relevance improvements driven by ML rankers and query understanding models.

Test Automation & Observability

• Build and scale automated data and AI testing frameworks integrated into CI/CD and model deployment pipelines.
• Define and enforce data quality SLAs and embed automated gates into pipeline orchestration (Airflow, dbt, Spark, etc.).
• Implement observability tooling for data pipelines and AI model inputs/outputs in collaboration with data and ML engineering.
• Drive adoption of synthetic data and data masking strategies to support safe testing.