Analytics Engineer

You'll Be Doing Define and evolve analytics modeling standards, architectural patterns, and semantic layers across domains to ensure consistent, trusted, and scalable analytics. Drive data mesh enablement across analytics by defining domain boundaries, ownership models, data products, and shared guardrails that scale self-service without fragmentation. Own data governance for analytics, including data privacy, RBAC, access patterns, and compliance requirements, ensuring secure and regulated use of data across tools and domains. Design and maintain data contracts, SLAs, and SLOs for analytics datasets and critical metrics, setting clear expectations for quality, freshness, and reliability. Own and continuously improve CI/CD, developer experience, and observability for analytics, making development, deployment, and debugging reliable and low-friction. Optimise analytics performance and cost across transformation and BI layers by improving query design, warehouse usage, materialisation

strategies, and asset lifecycle management. Own end-to-end lineage, metadata, and cataloging practices to ensure analytics assets are discoverable, well-documented, and easy to understand and trust. Act as the technical escalation point and mentor for Analytics Engineers, leading deep reviews and raising standards in modeling, testing, and architecture. Lead complex, cross-domain analytics initiatives independently from problem definition through delivery, coordinating with Data Engineering, Product, and business stakeholders. What You'll Bring 5+ years of experience as an Analytics Engineer (or equivalent), with proven ownership of complex, cross-domain analytics initiatives. Deep technical expertise in modern data warehouses (e.g., Snowflake), analytics transformation tooling (e.g., dbt), and self-service analytics platforms (e.g., Thought Spot, Looker). Strong command of SQL and data modeling, with the ability to design scalable transformation layers, semantic models, and reusable analytics patterns. Hands-on experience owning and improving CI/CD pipelines, developer workflows, testing strategies, and observability for analytics in production. Demonstrated ability to optimise analytics for performance, cost, and reliability across transformation and BI layers. Strong understanding of data mesh principles, including domain ownership, data products, and shared governance guardrails. Solid experience with data governance, including data privacy, RBAC, masking, and operating in regulated or security-conscious environments. Experience owning or driving lineage, metadata, and data catalog practices to improve discoverability, trust, and usability of analytics assets. Experience acting as a technical mentor and escalation point, providing guidance through deep code, modeling, and architectural reviews. Able to operate autonomously in ambiguous environments, set technical direction, and collaborate with stakeholders across Data Engineering, Product, and the wider business.

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