Data Engineer
Claranet
- Gloucester, Massachusetts, United States
- Gloucester, Massachusetts, United States
About
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
Duties and 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)
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, PostgreSQL, MySQL
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
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Languages
- English
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