Data / AI QE Lead — Retail eCommerce

Data / AI QE Lead — Retail eCommerce

Location-San Ramon, California or Beverly Hills, CA (5 Days Onsite)
Job Type-Long Term Contract
 

Role Summary

The Data / AI QE Lead will define the quality engineering strategy for data pipelines, machine learning models, and AI-powered features across the retail eCommerce platform. This role bridges traditional data quality assurance and emerging AI/ML validation disciplines, ensuring that customer-facing capabilities — including product recommendations, personalization, search relevance, demand forecasting, and pricing intelligence — perform accurately, fairly, and reliably at scale.

The role will build foundational QE practices for data and AI, partner with data engineering, data science, and product teams, and translate complex model behavior into measurable, business-aligned quality standards.

Key Responsibilities

Data Quality Engineering

Define and own the QE strategy for data assets including 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 (raw, curated, consumption), 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 features

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

Define rollback and circuit-breaker criteria for AI features that degrade customer experience

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 including CDP, CRM, marketing automation, and 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, representative testing environments

Establish version-controlled, repeatable test datasets for regression testing of ML models across release cycles

Cross-Functional Partnership

Collaborate with data scientists, data engineers, product managers, and business analysts to define acceptance criteria for data and AI deliverables

Champion a culture of data quality ownership across data producers and consumers in the eCommerce organization

Qualifications

Required

7 years in data or quality engineering, with at least 2 years leading a team or technical discipline

Proven experience testing data pipelines (batch and streaming) across modern data stack technologies (Spark, Kafka, Airflow, dbt, Snowflake, BigQuery, Databricks, or similar)

Hands-on experience with ML model evaluation techniques, including offline metrics and online experimentation (A/B testing)

Strong SQL skills and proficiency in Python for data validation scripting and test automation

Familiarity with eCommerce data domains: customer behavior, product catalog, order management, inventory, and digital marketing

Excellent ability to communicate data and AI quality concepts to technical and non-technical stakeholders

Preferred

Familiarity with Generative AI applications (RAG pipelines, LLM-powered features) and emerging AI QE practices

Knowledge of data privacy regulations (GDPR, CCPA) and their implications for test data management

Experience in high-scale eCommerce environments (peak traffic events, flash sales, seasonal demand spikes)