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Data Engineer
Sword GroupData Engineer at Sword seeking to build data pipelines and solutions. Collaborating with clients and teams for effective data integration in the Energy, Public, and Finance sectors.
Tech Stack
Tools & technologiesAzureETLPythonSparkSQL
About the role
Key responsibilities & impact- Design, build, and maintain scalable, secure, and high performing data pipelines using Azure Data Factory, Synapse, Fabric Pipelines, or Databricks
- Integrate data from multiple systems into Azure Data Lake Storage or OneLake, ensuring consistency, reliability, and performance
- Develop and maintain ELT/ETL processes using Azure-native tools and Fabric capabilities, optimising for cost and performance
- Build and maintain Microsoft Fabric artefacts including lakehouses, warehouses, notebooks, dataflows, and semantic models
- Perform advanced data transformations using SQL, Python, Spark, or Fabric Data Engineering experiences to prepare data for analytics and reporting
- Manage and optimise data models used by Power BI, ensuring good performance, relationships, quality, and reusability
- Support enterprise BI development by preparing datasets, semantic models, and reusable components within Power BI and Fabric
- Apply data governance and security best practices by implementing Purview classification, lineage, access controls, and policies
- Conduct data quality checks, data validation routines, and issue resolution to ensure trustworthy and reliable datasets
- Work closely with cross-functional teams (data architects, analysts, data scientists, product teams) to understand requirements and translate them into robust data solutions
- Engage directly with clients and stakeholders to clarify data needs, communicate progress, and explain technical concepts in a clear, accessible way
- Take ownership of more complex engineering tasks and assist senior engineers with solution components requiring deeper technical insight
- Mentor junior engineers by providing guidance, performing code reviews, and sharing best practices in engineering standards and Azure environments
- Contribute to CI/CD processes, version control practices, and automated deployment pipelines for data solutions
- Support the creation of documentation for pipelines, datasets, data models, and solution designs
- Troubleshoot pipeline failures, performance issues, and data inconsistencies, ensuring the stability and reliability of production workloads
- Stay current with emerging tools and capabilities across Azure, Microsoft Fabric, Power BI, Purview, and Microsoft Foundry
- Identify opportunities to improve efficiency, automation, scalability, or maintainability across existing pipelines and solutions
- Contribute to internal knowledge sharing, best-practice development, and engineering accelerators or templates
Requirements
What you’ll need- Deep understanding of Azure Data Factory, Synapse, Fabric, Power BI and Databricks
- Experience in Fabric/data architecture
- Experience in Fabric/data migrations
- Understanding of the requirements of stringent data governance
- Ability to consult with clients
- Proven experience of mentoring teams
- Experience of advanced data transformations using SQL, Python, Spark, or Fabric Data Engineering experiences to prepare data for analytics and reporting
Benefits
Comp & perks- Personalised Career Development: We create a development plan customised to your goals and aspirations, with a range of learning and development opportunities within a culture that encourages growth.
- Flexible working: Flexible work arrangements to support your work-life balance. We can’t promise to always be able to meet every request, however, are keen to discuss your individual preferences to make it work where we can.
- A Fantastic Benefits Package: This includes generous annual leave allowance, enhanced family friendly benefits, pension scheme, access to private health, well-being, and insurance schemes.
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
data pipelinesELTETLdata transformationsSQLPythonSparkdata modelsdata governanceCI/CD
Soft Skills
consultingmentoringcommunicationcollaborationproblem-solvingownershipguidanceclarityissue resolutionknowledge sharing