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Data & Analytics

Data Warehouse Engineer

Design and build governed analytical warehouse structures, ETL/ELT pipelines and dimensional models for reliable enterprise reporting and analytics.

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LocationLondon, UK

Working styleHybrid / client-aligned

Employment typeFull-time

Experience4–7 years

Technology environment

SQLSnowflakeMicrosoft FabricAzure SynapseDatabricksdbtSSISPower BI

Industry context

Banking & FinanceEducationMediaProfessional Services

Job description

The Data Warehouse Engineer will develop warehouse schemas, transformation pipelines and performance controls for enterprise analytical workloads.

Key responsibilities

Design fact and dimension models aligned to reporting requirements.
Build ETL/ELT pipelines, history handling and data-quality controls.
Optimise warehouse queries, storage and refresh performance.
Document lineage, source-to-target mappings and business definitions.
Support release, monitoring and production issue resolution.

Qualifications

4–7 years of data-warehouse, ETL/ELT or analytics-engineering experience.
Strong SQL and dimensional-modelling capability.
Hands-on experience with at least one modern warehouse or lakehouse platform.

Preferred qualifications

Snowflake, Fabric, Synapse or Databricks experience.
dbt or SSIS experience.
Experience with enterprise BI and governed semantic models.

Benefits & employment terms

Compensation, leave, pension and any role-specific benefits are confirmed during the recruitment process and stated in the written offer.
Any client-site, travel, security-screening or right-to-work requirements are confirmed before appointment.

Nature of working style

Work is organised around defined delivery outcomes, documented responsibilities, peer review and clear escalation paths.
Hybrid or client-site attendance varies by engagement and is confirmed before assignment.

Location

London-based with UK client-site collaboration where required.

Working at RC

Delivery standards shape the employee experience

Technology roles are organised around accountable delivery, professional engineering practices and clear client or project outcomes.

Client-impact work

Roles are connected to defined business or delivery outcomes rather than artificial internal assignments.

Professional craft

Engineering, consulting and delivery decisions are expected to be explainable, maintainable and grounded in the operating context.

Clear ownership

Responsibilities, interfaces, quality expectations and escalation paths should be visible across teams and engagements.

Continuous learning

Capability grows through real delivery, peer review, feedback and exposure to changing technologies and business environments.

Hiring journey

A structured process from application to decision

The exact interview sequence may vary by role, but candidates should understand the purpose of each stage and the position they are being assessed for.

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