{"schemaVersion":"jobsearcher.job.v1","id":"1119c3f3256b4c8805ad18c8","url":"https://jobsearcher.com/jobs/1119c3f3256b4c8805ad18c8","canonicalUrl":"https://jobsearcher.com/jobs/1119c3f3256b4c8805ad18c8","title":"Data Engineer","description":"Position Summary\r\nThe 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.\r\nRole Mission\r\nClaranet'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.\r\nObjectives & Key Results\r\nDeliver secure, scalable, and repeatable Azure data solutions aligned to financial services requirements\r\nEnsure data pipelines are reliable, performant, automated, and auditable\r\nSupport analytics and machine learning workloads through robust data engineering practices\r\nMaintain high standards of data quality, governance, documentation, and operational resilience\r\nDuties and Responsibilities\r\nIdentify and understand customer data-centric use cases within regulated financial services environments\r\nDesign and implement data ingestion, processing, and transformation pipelines on Azure\r\nBuild and maintain data pipelines for cleaning, normalisation, enrichment, and preparation\r\nApply appropriate data modelling techniques and architecture patterns, with a strong focus on medallion architecture\r\nOrchestrate, monitor, and optimise Azure Databricks jobs and Azure Data Factory pipelines across development, UAT, and production environments\r\nConfigure platforms, clusters, and compute resources to optimise performance, cost, and reliability\r\nUse automated CI/CD pipelines to manage, deploy, and version data artefacts and pipelines\r\nOperationalise workflows developed by analysts and data scientists\r\nSupport customers in adopting Azure data, analytics, and machine learning services\r\nEnsure secure storage, processing, and quality of customer data\r\nEnsure networking and security best practices are applied when designing and operating data solutions\r\nDesign solutions for processing large volumes of data using batch and streaming approaches\r\nCollaborate with analytics teams on data visualisation best practices and reporting enablement\r\nEnsure all solutions are well-documented, including pipelines, schemas, transformations, and operational runbooks\r\nFinancial Services & Regulatory Compliance\r\nEnsure all data engineering activities comply with financial services regulations and frameworks (e.g. FCA, PRA, DORA, ISO 27001)\r\nImplement GDPR, PII, and data protection controls across all data pipelines\r\nApply security best practices including encryption, access control, and audit logging\r\nSupport audits, risk assessments, and compliance reviews in collaboration with Quality and Security teams\r\nEnsure data solutions support operational resilience, business continuity, and audit requirements\r\nGovernance & Reporting\r\nMaintain accurate documentation of data pipelines, schemas, transformations, and deployment processes\r\nSupport data governance initiatives including lineage, metadata management, and access control\r\nContribute to service reporting, risk tracking, and continuous improvement actions\r\nEnsure data environments are audit-ready and aligned with governance standards\r\nTechnology Stack (Azure)\r\nCloud Platform: Microsoft Azure\r\nData Engineering & Analytics: Azure Databricks, Azure Data Factory, Azure Synapse Analytics (where applicable)\r\nMachine Learning & AI: Azure Machine Learning, Azure Document Intelligence\r\nDatabases: Microsoft SQL Server / Azure SQL Database, PostgreSQL, MySQL\r\nData Processing: Batch and streaming data pipelines\r\nSecurity & Governance: Role-based access control (RBAC), Data encryption and key management, Audit logging and monitoring\r\nDevOps: CI/CD pipelines for data artefacts and infrastructure\r\nTeams To Collaborate With\r\nCustomer Experience & Managed Service – Ensure consistent service delivery and operational support\r\nCustomer Success & Growth – Align data solutions with customer needs and growth objectives\r\nSecurity & Compliance – Ensure regulatory and data protection requirements are met\r\nCloud & Platform Engineering – Align data solutions with Azure platform and networking standards\r\nAnalytics & Data Science Teams – Support operationalisation of analytics and ML workloads\r\nBehavioural Competencies – Organisational & Behavioural Fit\r\nPositive mindset and enthusiasm for learning new technologies\r\nCollaborative and supportive team player\r\nStrong sense of ownership and accountability\r\nMethodical, analytical approach to problem solving\r\nStrong understanding of ethical data usage in regulated environments\r\nCritical Competencies – Technical Fit\r\nEssential\r\nStrong SQL skills\r\nProgramming experience with Python and/or Scala\r\nHands-on experience with Azure-based data platforms\r\nExperience designing, building, and maintaining data pipelines\r\nStrong understanding of data modelling (relational and analytical), including medallion architecture\r\nExperience orchestrating and optimising Databricks and Data Factory workloads\r\nExperience using CI/CD pipelines for data and analytics solutions\r\nStrong awareness of security, networking best practices, GDPR, and PII handling\r\nDesirable\r\nExperience with Azure Databricks in production environments\r\nFamiliarity with Azure Machine Learning and AI services\r\nExposure to data visualisation tools (e.g. Power BI)\r\nExperience with big data frameworks (Spark, Kafka)\r\nKnowledge of data governance, lineage, and metadata tooling\r\nShift & Working Pattern\r\nStandard business hours, with participation in an on-call rota as required\r\nOccasional 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\r\nJ-18808-Ljbffr","company":"Claranet","rawCompany":"claranet","city":"Gloucester","state":"MA","isRemote":false,"isActive":false,"createdAt":"2026-06-25T00:55:18.834Z","occupations":[{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1243.00","title":"Database Architects","slug":"database-architects"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Data Engineer","description":"Position Summary\r\nThe 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.\r\nRole Mission\r\nClaranet'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.\r\nObjectives & Key Results\r\nDeliver secure, scalable, and repeatable Azure data solutions aligned to financial services requirements\r\nEnsure data pipelines are reliable, performant, automated, and auditable\r\nSupport analytics and machine learning workloads through robust data engineering practices\r\nMaintain high standards of data quality, governance, documentation, and operational resilience\r\nDuties and Responsibilities\r\nIdentify and understand customer data-centric use cases within regulated financial services environments\r\nDesign and implement data ingestion, processing, and transformation pipelines on Azure\r\nBuild and maintain data pipelines for cleaning, normalisation, enrichment, and preparation\r\nApply appropriate data modelling techniques and architecture patterns, with a strong focus on medallion architecture\r\nOrchestrate, monitor, and optimise Azure Databricks jobs and Azure Data Factory pipelines across development, UAT, and production environments\r\nConfigure platforms, clusters, and compute resources to optimise performance, cost, and reliability\r\nUse automated CI/CD pipelines to manage, deploy, and version data artefacts and pipelines\r\nOperationalise workflows developed by analysts and data scientists\r\nSupport customers in adopting Azure data, analytics, and machine learning services\r\nEnsure secure storage, processing, and quality of customer data\r\nEnsure networking and security best practices are applied when designing and operating data solutions\r\nDesign solutions for processing large volumes of data using batch and streaming approaches\r\nCollaborate with analytics teams on data visualisation best practices and reporting enablement\r\nEnsure all solutions are well-documented, including pipelines, schemas, transformations, and operational runbooks\r\nFinancial Services & Regulatory Compliance\r\nEnsure all data engineering activities comply with financial services regulations and frameworks (e.g. FCA, PRA, DORA, ISO 27001)\r\nImplement GDPR, PII, and data protection controls across all data pipelines\r\nApply security best practices including encryption, access control, and audit logging\r\nSupport audits, risk assessments, and compliance reviews in collaboration with Quality and Security teams\r\nEnsure data solutions support operational resilience, business continuity, and audit requirements\r\nGovernance & Reporting\r\nMaintain accurate documentation of data pipelines, schemas, transformations, and deployment processes\r\nSupport data governance initiatives including lineage, metadata management, and access control\r\nContribute to service reporting, risk tracking, and continuous improvement actions\r\nEnsure data environments are audit-ready and aligned with governance standards\r\nTechnology Stack (Azure)\r\nCloud Platform: Microsoft Azure\r\nData Engineering & Analytics: Azure Databricks, Azure Data Factory, Azure Synapse Analytics (where applicable)\r\nMachine Learning & AI: Azure Machine Learning, Azure Document Intelligence\r\nDatabases: Microsoft SQL Server / Azure SQL Database, PostgreSQL, MySQL\r\nData Processing: Batch and streaming data pipelines\r\nSecurity & Governance: Role-based access control (RBAC), Data encryption and key management, Audit logging and monitoring\r\nDevOps: CI/CD pipelines for data artefacts and infrastructure\r\nTeams To Collaborate With\r\nCustomer Experience & Managed Service – Ensure consistent service delivery and operational support\r\nCustomer Success & Growth – Align data solutions with customer needs and growth objectives\r\nSecurity & Compliance – Ensure regulatory and data protection requirements are met\r\nCloud & Platform Engineering – Align data solutions with Azure platform and networking standards\r\nAnalytics & Data Science Teams – Support operationalisation of analytics and ML workloads\r\nBehavioural Competencies – Organisational & Behavioural Fit\r\nPositive mindset and enthusiasm for learning new technologies\r\nCollaborative and supportive team player\r\nStrong sense of ownership and accountability\r\nMethodical, analytical approach to problem solving\r\nStrong understanding of ethical data usage in regulated environments\r\nCritical Competencies – Technical Fit\r\nEssential\r\nStrong SQL skills\r\nProgramming experience with Python and/or Scala\r\nHands-on experience with Azure-based data platforms\r\nExperience designing, building, and maintaining data pipelines\r\nStrong understanding of data modelling (relational and analytical), including medallion architecture\r\nExperience orchestrating and optimising Databricks and Data Factory workloads\r\nExperience using CI/CD pipelines for data and analytics solutions\r\nStrong awareness of security, networking best practices, GDPR, and PII handling\r\nDesirable\r\nExperience with Azure Databricks in production environments\r\nFamiliarity with Azure Machine Learning and AI services\r\nExposure to data visualisation tools (e.g. Power BI)\r\nExperience with big data frameworks (Spark, Kafka)\r\nKnowledge of data governance, lineage, and metadata tooling\r\nShift & Working Pattern\r\nStandard business hours, with participation in an on-call rota as required\r\nOccasional 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\r\nJ-18808-Ljbffr","datePosted":"2026-06-25T00:55:18.834Z","dateModified":"2026-06-25T00:55:18.834Z","hiringOrganization":{"@type":"Organization","name":"Claranet","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Gloucester","addressRegion":"MA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"1119c3f3256b4c8805ad18c8"},"url":"https://jobsearcher.com/jobs/1119c3f3256b4c8805ad18c8"}}