Senior Data Engineer

Role Summary
Build and operate the pipelines that ingest, normalise, enrich and publish product, price, promotion and inventory data to downstream commerce channels. The role owns data correctness end to end: from source systems through transformation and mapping logic to channel-specific feed output, with the accuracy and freshness guarantees each channel demands.
Key Responsibilities

• Design, build and maintain batch and streaming pipelines for product, price, inventory and promotion data across multiple regions and locales.
• Implement and maintain attribute mapping logic - category mapping, product-attribute mapping and transformation rules - between internal catalogue models and channel-specific specifications.
• Build delta and incremental sync mechanisms for high-frequency inventory and price updates, meeting agreed latency targets.
• Model and maintain variant and grouping structures (item grouping, variant attributes, parent-child relationships) so products render correctly on each destination channel.
• Develop data quality checks, validation rules and reconciliation reporting to detect missing, malformed or non-compliant attributes before publication.
• Support onboarding of new destination channels: analyse the target specification, map required and optional fields, identify gaps and drive remediation with upstream data owners.
• Handle multi-region and multi-locale requirements including localisation, currency, tax and data residency constraints.
• Instrument pipelines with monitoring, alerting and lineage so failures and drift are caught early.
• Partner with feed management platform vendors and internal stakeholders on integration, configuration and troubleshooting.
• Document data contracts, field mappings and operational runbooks.

• Strong SQL and at least one programming language for data work - Python, Scala or Java.
• Hands-on experience building production ETL/ELT pipelines and orchestrating them with a scheduler or workflow engine (Airflow, Dagster, ADF or equivalent).
• Experience with streaming or event-driven data movement - Kafka or a comparable message bus.
• Working knowledge of a cloud data platform (Azure, AWS or GCP) including managed compute, storage and serverless functions.
• Proficiency with structured and semi-structured formats: CSV, XML, JSON, Parquet; comfortable with large-file, delimiter and encoding edge cases.
• Demonstrated experience implementing data quality frameworks, schema validation and reconciliation logic.
• Solid data modelling fundamentals - normalisation, dimensional modelling and slowly changing data.
• Version control, CI/CD and infrastructure-as-code practices applied to data workloads.
• bility to read and interpret third-party API and feed specifications and translate them into implementation requirements.

• Experience in retail, e-commerce, beauty, fashion or marketplace environments.
• Direct experience with product catalogue or PIM/PXM systems and product data governance.
• Familiarity with feed management platforms such as Productsup, Lengow, Feedonomics or Channable.
• Experience publishing to commerce destinations - Google Merchant Center, marketplace catalogues, social commerce platforms or AI/agentic shopping surfaces.
• Understanding of GTIN, SKU and identifier hygiene, and channel product-approval workflows.
• Exposure to data residency, GDPR and regional compliance requirements in a multi-market rollout.