About Claranet
Founded at the beginning of the dot.com bubble in 1996, our CEO Charles Nasser had a light bulb moment to develop a truly customer-focused IT business. Since then, Claranet has grown from an Internet Service Provider (ISP) in the UK to being one of the leading business modernisation experts, who deliver solutions across 11+ countries.

At Claranet, we’re experienced in implementing progressive technology solutions which help our customers solve their epic business challenges. We’re committed to understanding their problems, delivering answers quickly, and making a lasting impact to their business.

We are agile, focused and experienced in business modernisation. Our approach helps customers make genuine, significant shifts in their business strategy, to deliver financial savings, boost innovation, and create a resilient business. We continually invest in our people and the latest technologies, so our customers get peace of mind knowing that they have access to the best talent and services.

In the UK we have over 500 staff working in London, Gloucester, Warrington, Leeds or as homeworkers.

Working For Claranet
Here at Claranet we pride ourselves on going the extra mile for and with our employees (yes, we really mean it).
We offer an extensive benefits package that you can tailor to your needs, inclusive of a matching contribution pension scheme, healthcare, insurance, dental, discounted gyms and app supported benefit access.

But what we think makes us different is ‘Team Claranet,’ our dedicated internal part of the business that supports you with matters close to your heart. We proudly support local charities in each of our office locations, support employees with paid charity leave, organise key charity fundraising event per year and have a dedicated committee responsible for supporting employee’s fundraising efforts.

Our Vision
Our vision is to become the most trusted technology solutions partner; renowned for being the best and brightest, having lasting impact with our customers and delivering exceptional returns to our stakeholders.

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.

The Data Engineer works closely with customers, analysts, and platform teams to operationalise data pipelines, support analytics and machine learning workloads, and ensure data environments are reliable, auditable, performant, and compliant.

Role Mission
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.

Objectives & Key Results

• 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

Essential Roles & Responsibilities

• 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

Financial Services & Regulatory Compliance

• 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

Governance & Reporting

• 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

Technology Stack (Azure)
You will work primarily with the following technologies:

Cloud Platform

• Microsoft Azure

Data Engineering & Analytics

• Azure Databricks (development, UAT, and production)
• Azure Data Factory
• Azure Synapse Analytics (where applicable)

Machine Learning & AI

• Azure Machine Learning (limited non‑production usage)
• Azure Document Intelligence

Databases

• Microsoft SQL Server / Azure SQL Database (primary platforms)
• PostgreSQL (limited use)
• MySQL (limited use)

Data Processing

• Batch and streaming data pipelines

Security & Governance

• Role‑based access control (RBAC)
• Data encryption and key management
• Audit logging and monitoring

DevOps

• CI/CD pipelines for data artefacts and infrastructure

Teams To Collaborate With

• Customer Experience & Managed Service – Ensure consistent service delivery and operational support
• Customer Success & Growth – Align data solutions with customer needs and growth objectives
• Security & Compliance – Ensure regulatory and data protection requirements are met
• Cloud & Platform Engineering – Align data solutions with Azure platform and networking standards
• Analytics & Data Science Teams – Support operationalisation of analytics and ML workloads

Behavioural Competencies – Organisational & Behavioural Fit

• Positive mindset and enthusiasm for learning new technologies
• Collaborative and supportive team player
• Strong sense of ownership and accountability
• Methodical, analytical approach to problem solving
• Strong understanding of ethical data usage in regulated environments

Critical Competencies – Technical Fit
Essential:

• Strong SQL skills
• Programming experience with Python and/or Scala
• Hands‑on experience with Azure‑based data platforms
• Experience designing, building, and maintaining data pipelines
• Strong understanding of data modelling (relational and analytical), including medallion architecture
• Experience orchestrating and optimising Databricks and Data Factory workloads
• Experience using CI/CD pipelines for data and analytics solutions
• Strong awareness of security, networking best practices, GDPR, and PII handling

Desirable:

• Experience with Azure Databricks in production environments
• Familiarity with Azure Machine Learning and AI services
• Exposure to data visualisation tools (e.g. Power BI)
• Experience with big data frameworks (Spark, Kafka)
• Knowledge of data governance, lineage, and metadata tooling

Shift & Working Pattern:

• Standard business hours, with participation in an on‑call rota as required
• Occasional weekend engineering coverage will be required, typically limited to a small number of planned weekends per year, to support business continuity, resilience testing, or disaster recovery activities