Analytics Engineer

Who you are

• 3+ years in a data platform, data engineering, analytics engineering, DataOps
• Breadth over depth in a single tool - comfortable switching between topics, picking up unfamiliar problems, with a wide base of technical knowledge across the data stack
• Strong SQL and Python skills, with a real understanding of how databases work (query execution, performance tuning, storage, access and permission models) and enough dbt experience to review others' work and set standards
• Working knowledge of the AWS data stack (Redshift, S3, IAM, Athena, Glue) or equivalent, plus experience with lakehouse architectures (Apache Iceberg, Delta, etc), pipeline orchestration (Apache Airflow or equivalent), and modern ingestion/ELT tooling (Airbyte, Fivetran, or equivalent)
• Exposure to Infrastructure as Code (Pulumi, Terraform, or equivalent), and confidence navigating internal tooling and existing codebases
• Strong communication and writing. You can move between a stakeholder conversation about a problem and a technical discussion about how to solve it
• Comfortable writing documentation, guides, and architecture decisions
• Initiative and autonomy to drive work forward independently after onboarding, raising blockers early
• Fluent English, written and spoken; based in Europe with workable timezone overlap

What the job involves

• You'll join a cross-functional Data Platform Team serving Emma's broader Data and Tech organisation, with internal users spanning analysts, analytics engineers, data scientists and data engineers
• The team's mission is to make that work faster, safer, and more reliable - through observability, access control, engineering standards, code reviews, internal tooling, and AI adoption across the full pipeline from ingestion to BI tool consumption
• While the primary focus is analytics engineering tooling and process, you'll operate across the full stack, working closely with the Staff Analytics and Data Engineers to scope, build, and ship, while raising the bar for how the team builds
• The role rewards breadth and initiative over deep specialisation in one layer, requiring frequent context-switching and comfort with unfamiliar problems
• Reliability, Standards & Governance
• Own and improve monitoring, alerting, and observability across the data platform, so failures are caught early and pipeline/model health is visible to teams
• Contribute to architecture discussions: propose improvements, document trade-offs (ADRs, RFCs), and help decide what to build, refactor, or retire
• Set, document, and enforce engineering standards and best practices across our lakehouse, orchestration layer, data warehouse, and reporting systems, including code review culture
• Enablement & Internal Tooling
• Write clear guides, standards, and documentation that help colleagues across the Data domain work more efficiently and consistently, driving alignment through knowledge-sharing forums
• Build and maintain internal tooling that removes friction for the teams you serve (e.g. AI-augmented workflows, extending observability and quality frameworks)
• Support AI adoption within our data infrastructure, in collaboration with the broader tech division
• Hands-On Pipeline & model development
• Enhance and build on our Redshift data warehouse using dbt and Paradime
• Orchestrate execution and dependencies between up/downstream pipelines (MWAA, Paradime), provisioning infrastructure via IaC (Pulumi, Terraform) so changes stay reproducible and version-controlled
• Contribute to our ingestion pipelines across three core patterns - simple ELT, containerised Python, and event-based - landing data reliably into our medallion lakehouse (S3, Glue, Iceberg)