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Responsibilities
Data Quality Engineering
Required
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
- 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
- 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
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