Data Engineer

CUBE

Data Engineer for CUBE's Data and AI Engineering team, designing and building data pipelines for regulatory intelligence platform. Collaborate with data architects and engineers in a fast-paced environment.

Posted 4/21/2026full-timeLondon • 🇬🇧 United KingdomMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

AirflowApacheAzureCloudETLPythonSparkSQLTerraform

About the role

Key responsibilities & impact

Design and build data pipelines - Build, maintain, and optimise data pipelines that ingest, transform, and deliver structured and unstructured regulatory content across our platform estate.
Transform and model data - Apply transformation logic that converts raw source data into clean, reliable, semantically consistent assets ready for analytics and AI consumption.
Implement data quality and observability practices - Instrument pipelines with monitoring, alerting, and data quality checks that catch problems early and maintain platform trust.
Collaborate with architects and platform engineers - Work closely with the Principal Data Architect and Head of Data Platform to implement patterns that align with our architectural direction.
Support integration and migration work - Contribute to source-to-target mapping and pipeline development for ongoing platform consolidation.
Champion engineering best practices - Write code that others can maintain: version-controlled, tested, documented, and built for production.
Contribute to platform scalability and cost efficiency - Identify and resolve performance bottlenecks, redundancies, and inefficiencies in existing pipeline infrastructure.
Build for AI readiness - Understand how downstream AI/ML workloads consume data and design pipelines that support feature engineering, model training, and inference requirements.

Requirements

What you’ll need

3+ years of experience in data engineering or a closely related role.
Strong SQL and Python skills—you write production-quality code, not just scripts.
Hands-on experience building and maintaining data pipelines in cloud environments.
Familiarity with ETL/ELT patterns, orchestration tools (e.g. Apache Airflow, dbt, Azure Data Factory), and data transformation frameworks.
Experience working with both structured and unstructured or semi-structured data.
Understanding of data quality principles—you know what a bad pipeline looks like and how to fix it.
Comfort with version control, CI/CD practices, and engineering-grade delivery.
Experience with Microsoft Azure data services - Azure Data Factory, Synapse Analytics, Data Lake Storage, Fabric.
Familiarity with Apache Spark for large-scale data processing.
Exposure to data modelling concepts - normalisation, dimensional design, entity-relationship patterns.
Background in platform integration, data migration, or M&A consolidation work.
Experience building pipelines that support AI/ML workloads, including feature stores or model training infrastructure.
Knowledge of data governance practices - lineage, cataloguing, access control, compliance.
Familiarity with infrastructure-as-code tooling (e.g. Terraform).
Exposure to regulatory, financial services, or compliance data domains.

Benefits

Comp & perks

Diversity, collaboration, and purpose are the heartbeat of our success.

ATS Keywords

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Hard Skills & Tools

SQLPythondata engineeringdata pipelinesETLELTdata transformationdata qualitydata modellinginfrastructure-as-code

Soft Skills

collaborationproblem-solvingcommunicationattention to detailadaptabilitycritical thinkingtime managementteamworkleadershipmentoring