Senior Analytics Engineer - Sponsored Content & Products (all genders)

Requirements

• Data Transformation Tooling: Practical, hands-on experience utilizing dbt (Data Build Tool) for managing version-controlled analytical modeling codebases, validation schemas, and deployment workflows
• Domain Expertise: Prior experience working within an eCommerce, retail tech, or ad-tech ecosystem, with a solid understanding of web analytics tracking, funnel parameters, or ad performance metrics
• Your perks at a glance: Visit our benefits page .
• Simply apply online via our career page - we will get back to you as soon as possible!
• A Place Where You Can Be You
• We take it as our responsibility to create an environment where everyone feels welcome, exactly as they are.
• Different backgrounds and perspectives make us stronger and shape our culture in ways that matter.
• What we stand for internally, we stand for as a brand: acceptance, inclusion, and a fairer approach to fashion.

Additional Information

The Sponsored Content & Products team is a key growth driver. Its mission is to build highly scalable self-serve cost-per-click (CPC) bidding marketplaces that empower brands to efficiently boost their visibility in real time right at the digital point of sale. While operating within an existing ecosystem, the architectural transition from a single monolith into four specialized product teams provides a highly autonomous, greenfield-like environment where your decisions will directly shape our future state and enable us to multiply our Retail Media revenue. Own the Semantic Layer: Design, build, and maintain production-grade analytical data transformations and metrics definitions within Google BigQuery to enable robust performance and budget reporting for our bidding marketplaces Data Quality & Validation Governance: Establish rigorous, automated data validation frameworks and schema checks to shield public-facing reporting dashboards and analytical tables from upstream variance or structural drift Deconstruct Legacy Transformations: Lead the migration of complex analytical transformation rules and data aggregation queries directly out of historical legacy application repositories into modular, decoupled analytics environments Resolve Catalog & Domain Coupling: Partner with Data Engineers to engineer clean data modeling boundaries around shared catalog assets (such as products and brands) that connect the decentralized product stack with core platform endpoints. Technical Stewardship: Set analytics engineering standards, direct code-reuse conventions, and establish reusable data blueprints within the team Data Transformation & Semantic Modeling Principles: Expert in designing modular and performance-optimized analytical data models , and building scalable incremental transformations Cloud Data Warehousing: Advanced SQL mastery and extensive native experience within cloud-based big data platforms (e.g. Google BigQuery) Orchestration Environment Integration: Practical working familiarity with modern workflow orchestrators like Dagster to synchronize data assets and event-driven pipeline schedules Data Validation & Quality Engineering: Extensive experience writing declarative data checks, regression validation assertions, and data quality tests to enforce strict metric definition compliance CI/CD & Modern Engineering Practices: Proficiency with Git version control , structured code review flows, and automated deployment infrastructure like GitLab CI/CD pipelines Information Architect Mindset: A structural developer who excels at transforming loose business descriptions into highly structured, reliable, and standardized metrics environments Effective Communicator: Able to cleanly convey complex database schemas, transformation choices, and analytical definitions to software developers High Autonomy & Proactive Ownership: Highly driven to establish data blueprints within a technical greenfield context

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