Senior Analytics Engineer

Quick Facts

• Role: Senior Analytics Engineer (contract, min. 3–4 months)

• Collaboration: Directly with Head of Operations & Data and an analyst

• Stack (current/migrating): BigQuery, Cloud Run, Cloud Storage; dataform; migrating to dlt + Dagster + dbt; BI in Looker / Data Studio

Description

You will build a well-organized analytics data layer that turns today’s inconsistent models, manual spreadsheets, and scattered knowledge into a production-ready transformation layer. The focus is on dimensional modeling, shared KPI definitions, predictable delivery timelines, and repeatable analysis so business discussions shift from number correctness to decision-making. You will also design structures that enable safe, repeatable AI querying and prepare the BI layer.

Responsibilities

• Audit the current state: inventory data, scripts, dashboards, and KPI definitions to find inconsistencies and manual work

• Align with business on KPI definitions and information needs; build priorities and understand the final mart scope

• Design target data architecture (raw/staging/mart, dimensional + gold layers), including naming conventions and ownership rules

• Create an AI-ready layer with wide tables (OBT), metadata, and definitions tailored to specific questions

• Implement dimensional models in priority order, starting with areas with the most manual work; deliver production-ready results

• Add tests for critical models and implement alerting

• Apply GDPR/PII controls: minimization, retention, and access controls for children and parents’ data

• Handover: collaborate on a shared codebase with an analyst, provide reviews, and document to keep the model maintainable

Requirements

• Minimum 5 years of experience as a Data/Analytics Engineer, including ownership of a data warehouse or key modeling layer

• Advanced BigQuery with deliberate cost/performance management

• Transformation as code using dataform or dbt (versioning, tests, documentation, CI)

• Experience unifying KPI definitions across the organization

• Experience preparing models/metadata/definitions for safe, repeatable AI work

• Availability during team working hours (8–9 to 16–17)

Benefits

• Work closely with operations & data leadership and an analyst on a shared codebase

• Modern data stack and migration from dataform toward dlt + Dagster + dbt

• Emphasis on production readiness, testing/alerting, and privacy-by-design for sensitive data