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Tech Stack
Tools & technologiesAirflowApacheAzureCloudETLPythonSparkSQLTerraform
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
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
SQLPythondata engineeringdata pipelinesETLELTdata transformationdata qualitydata modellinginfrastructure-as-code
Soft Skills
collaborationproblem-solvingcommunicationattention to detailadaptabilitycritical thinkingtime managementteamworkleadershipmentoring