Senior Data Engineer

Essential Requirements:

Experience & Platform

• 10+ years of professional data engineering experience in enterprise environments.
• Demonstrated hands-on experience across the Microsoft Fabric platform including Lakehouse, Warehouse, Data Factory, Data Engineering (Spark), OneLake, and Power BI.
• Proven experience with Azure data services including Azure Data Factory, ADLS Gen2, Azure Databricks, and Azure Synapse Analytics.
• Strong proficiency in PySpark, Python, and SQL for large-scale data transformation and pipeline development.
• Practical knowledge of Microsoft Purview for data governance, cataloguing, lineage, and sensitivity classification.

Dynamics 365

• In-depth understanding of Dynamics 365 data structures including the Dataverse entity model, table relationships, option sets, and metadata.
• Hands-on experience with D365 Finance & Operations and/or Customer Engagement data architecture and common data entities.
• Practical experience implementing Synapse Link for Dataverse or equivalent D365 data export mechanisms for analytics workloads.
• Ability to interpret D365 functional configuration and translate it into sound data engineering design.

DevOps & Engineering Practice

• Demonstrated experience designing and maintaining CI/CD pipelines in Azure DevOps for data platform delivery.
• Proficiency with Git-based source control, branching strategies, and code review workflows.
• Familiarity with Infrastructure-as-Code (Bicep or Terraform) for automated Azure resource provisioning.
• Experience with automated testing for data pipelines, including schema validation and data quality assertions.
• Solid understanding of data modelling principles including star schema, slowly changing dimensions, and medallion architecture.
• Strong communication skills — able to engage technical and non-technical stakeholders effectively across delivery teams

Desirable criteria:

Legacy Systems Integration

• Proven track record migrating or integrating on-premises or legacy ERP/CRM systems into cloud-native data platforms.
• Experience with heterogeneous source systems including SQL Server, Oracle, SAP, flat file/SFTP ingestion, and REST APIs.
• Ability to assess technical debt and design pragmatic lift-and-shift or re-architecture migration patterns.

Data Science & ML Ops

• Hands-on experience with Fabric Data Science workloads, MLflow experiment tracking, and model operationalisation.
• Ability to prepare feature engineering pipelines and curated datasets to support AI and ML use cases.

Certifications (any of the following valued):

• DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric
• DP-600: Implementing Analytics Solutions Using Microsoft Fabric
• DP-500: Designing and Implementing Enterprise-Scale Analytics Solutions
• PL-300: Microsoft Power BI Data Analyst
• SC-400: Microsoft Information Protection Administrator (Purview)
• AZ-204 / AZ-400: Azure Developer or DevOps Engineer Expert
• MB-700: Microsoft Dynamics 365 Finance and Operations Apps Solution Architect

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We are seeking an experienced Data Engineer with deep expertise across the Microsoft Fabric and Azure data ecosystem to design, build, and operationalise enterprise-grade data platforms. The successful candidate will drive end-to-end pipeline development, govern data assets through Microsoft Purview, deliver analytics via Power BI, and apply Azure-native data services to solve complex integration and scalability challenges.

A thorough understanding of Dynamics 365 data structures — including Dataverse, D365 Finance & Operations, and Customer Engagement entity models — is essential. The role demands strong DevOps practice, including the design and maintenance of CI/CD pipelines that underpin reliable, automated delivery of data platform components.

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Akkodis is supporting a significant organisation transformation, transitioning a 20-year-old legacy system into contemporary cloud services. The transformation is reliant on the accurate, secure, and efficient migration of the legacy data into the new systems

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