Data QE Lead

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 eCommerce Platform Integration Testing 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 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 Martech and Personalization space is a must. 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