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Data & Analytics

Data Engineer

Build production-grade ingestion, transformation and orchestration pipelines that make enterprise data reliable and usable across reporting, analytics and operational workflows.

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LocationLondon, UK

Working styleHybrid

Employment typeFull-time

Experience3–5 years

Technology environment

PythonSQLPySparkAzure Data FactoryDatabricksdbtAirflowGit

Industry context

Banking & FinanceEducationMediaProfessional Services

Job description

The Data Engineer will develop and support reusable data pipelines, transformation logic and quality controls for cloud and enterprise data platforms.

The role requires practical engineering discipline around observability, recoverability, performance, schema change and deployment rather than one-off data movement.

Key responsibilities

Develop batch and incremental ingestion pipelines from databases, APIs, files and event sources.
Build transformation models, data-quality checks, orchestration and failure-recovery logic.
Optimise SQL, Spark and pipeline workloads for performance and cost.
Work with analysts and engineers to define source-to-target mappings, data contracts and ownership.
Contribute to CI/CD, code review, monitoring and production support for data workloads.

Qualifications

3–5 years of professional data-engineering, ETL/ELT or closely related software-engineering experience.
Strong SQL and working Python skills.
Hands-on experience with at least one cloud data platform or distributed processing framework.
Understanding of dimensional modelling, data quality, orchestration and version control.

Preferred qualifications

Experience with Databricks, Microsoft Fabric, dbt, Airflow or equivalent tooling.
Exposure to streaming, data observability or governance platforms.
Experience supporting production data pipelines in multi-system environments.

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

Engineers work in cross-functional data teams with peer review and defined production ownership.
Hybrid collaboration is expected for design reviews, stakeholder workshops and delivery planning.

Location

London-based hybrid role with occasional UK client-site activity depending on the engagement.

Working at RC

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

Client-impact work

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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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