Data Engineer

Position Summary

The Data Engineer is responsible for designing, building, and maintaining secure, scalable data solutions for financial services customers, ensuring all data‑centric use cases are delivered in line with regulatory, security, and operational requirements. The role focuses on Azure‑based data platforms, supporting ingestion, transformation, processing, and quality assurance across structured and unstructured data sources.

Claranet’s strategy is to build long‑term, trusted relationships with financial services customers by delivering market‑leading, integrated managed services. As part of the Data Practice, the Data Engineer supports customer IT and data transformations by delivering highly scalable, secure, and compliant Azure data platforms.

• Deliver secure, scalable, and repeatable Azure data solutions aligned to financial services requirements
• Ensure data pipelines are reliable, performant, automated, and auditable
• Support analytics and machine learning workloads through robust data engineering practices
• Maintain high standards of data quality, governance, documentation, and operational resilience

• Identify and understand customer data‑centric use cases within regulated financial services environments
• Design and implement data ingestion, processing, and transformation pipelines on Azure
• Build and maintain data pipelines for cleaning, normalisation, enrichment, and preparation
• Apply appropriate data modelling techniques and architecture patterns, with a strong focus on medallion architecture
• Orchestrate, monitor, and optimise Azure Databricks jobs and Azure Data Factory pipelines across development, UAT, and production environments
• Configure platforms, clusters, and compute resources to optimise performance, cost, and reliability
• Use automated CI/CD pipelines to manage, deploy, and version data artefacts and pipelines
• Operationalise workflows developed by analysts and data scientists
• Support customers in adopting Azure data, analytics, and machine learning services
• Ensure secure storage, processing, and quality of customer data
• Ensure networking and security best practices are applied when designing and operating data solutions
• Design solutions for processing large volumes of data using batch and streaming approaches
• Collaborate with analytics teams on data visualisation best practices and reporting enablement
• Ensure all solutions are well‑documented, including pipelines, schemas, transformations, and operational runbooks

• Ensure all data engineering activities comply with financial services regulations and frameworks (e.g. FCA, PRA, DORA, ISO 27001)
• Implement GDPR, PII, and data protection controls across all data pipelines
• Apply security best practices including encryption, access control, and audit logging
• Support audits, risk assessments, and compliance reviews in collaboration with Quality and Security teams
• Ensure data solutions support operational resilience, business continuity, and audit requirements

• Maintain accurate documentation of data pipelines, schemas, transformations, and deployment processes
• Support data governance initiatives including lineage, metadata management, and access control
• Contribute to service reporting, risk tracking, and continuous improvement actions
• Ensure data environments are audit‑ready and aligned with governance standards

• Cloud Platform: Microsoft Azure
• Data Engineering & Analytics: Azure Databricks, Azure Data Factory, Azure Synapse Analytics (where applicable)
• Machine Learning & AI: Azure Machine Learning, Azure Document Intelligence
• Databases: Microsoft SQL Server / Azure SQL Database, Postgre
  
  SQL, MySQL
• Data Processing: Batch and streaming data pipelines
• Security & Governance: Role‑based access control (RBAC), Data encryption and key management, Audit logging and monitoring
• Dev Ops: CI/CD pipelines for data artefacts and infrastructure

• Customer Experience & Managed Service – Ensure consistent service delivery and operational support
• Customer Success & Growth – Align data solutions with customer…