Lead ETL Tester​/AI QE Lead

Headquartered in California, U.S.A., GSPANN is a leading provider of consulting and IT services to global clients. We specialize in helping clients transform their IT capabilities, optimize business practices, and drive operational efficiency across industries such as retail, high-technology, and manufacturing. With five global delivery centers and over 2500 employees, we combine the personalized approach of a boutique consultancy with the extensive capabilities of a large IT services firm.

• 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

• 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

• 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

• 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

• 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

• 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, Big Query, 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…