Senior Data Engineer (Python/Azure)

What We Offer

• A highly collaborative and engineering-focused culture.
• A strong bias for action with minimal bureaucracy.
• The opportunity to engineer the core data engine driving product discovery for millions of daily active users using Azure, Kafka, Spark, Elasticsearch, and Medallion Data Architecture.

Parser is looking for an experienced Senior Data Engineer to join a client delivery team working on the E-commerce Search & Product Discovery Platform for a major multinational retailer. The client operates across multiple regions-including the UK, Europe, and Asia-offering groceries, clothing, electronics, and consumer goods via physical stores and large-scale e-commerce platforms, alongside financial, mobile, and fuel services.

As a Senior Data Engineer at Parser, you will construct the core data platform powering search relevance and optimization initiatives. This foundation aggregates multi-source event streams and telemetry data to ensure it is clean, traceable, and readily accessible for downstream applications-supporting automated search problem discovery, offline change evaluation, and human relevance testing. Joining in week 1, you will collaborate with the client's analytics team to establish the platform architecture and build key data pipeline components to deliver a functional prototype by week 2.

Key Responsibilities

• Data Foundation Architecture: Build and scale the shared search data foundation using a
  Medallion Architecture: Bronze (raw source ingestion), Silver (cleaned, validated, and standardized datasets), and Gold (business-ready data for relevance analysis and offline evaluation).
• Search Telemetry Integration: Integrate and join complex search data sources (clickstream, query logs, product interactions) in close collaboration with client analytics and data science teams.
• Platform Evaluation: Review existing data pipelines, dashboards, and technology stacks (Azure, Kafka, Elasticsearch) during week 1 to define and agree on optimal data lake architecture.
• Data Quality & Lineage: Implement robust data quality checks and end-to-end data lineage to ensure datasets can be audited, validated, and reused across search initiatives.
• Relevance Feedback Loops: Supply high-quality datasets to power discovery tools and offline evaluation frameworks, ingesting human relevance ratings back into the core data foundation.
• Engineering Excellence: Write clean, maintainable, and well-tested code, documenting design decisions and pipeline architectures for seamless handover.