Key Responsibilities Design, build, and maintain scalable data pipelines and ingestion frameworks. Integrate and consolidate data from multiple SaaS applications, APIs, and operational systems. Develop and maintain Microsoft Fabric Lakehouse, Warehouse, Dataflow, and Pipeline solutions. Transform and model raw data into trusted, reporting-ready datasets. Build and optimise semantic models that power business-critical Power BI reporting. Collaborate with stakeholders to define KPIs, reporting requirements, and analytics solutions. Identify and resolve data quality, reconciliation, and consistency challenges. Lead technical and architectural decision-making across complex data initiatives. Monitor, optimise, and improve pipeline performance, reliability, and scalability. Promote best practices in data governance, modelling, and analytics architecture. Job Experience and Skills Required Education: Bachelor's Degree in Computer Science, Information Systems, Engineering, or a related field. Relevant practical experience will also be considered. Experience: 57 years' experience in Data Engineering or Analytics Engineering roles. Proven experience delivering production-grade solutions using Microsoft Fabric. Strong background in data warehousing, Lakehouse architectures, and dimensional data modelling. Experience integrating data from REST APIs, SaaS platforms, and operational systems. Demonstrated ability to engage directly with business stakeholders and translate requirements into technical solutions. Experience working independently in complex, fast-moving environments. Technical Skills: Microsoft Fabric (Lakehouse, Warehouse, Dataflows, Pipelines, Semantic Models). Advanced SQL development, optimisation, and performance tuning. Python for data engineering, automation, and integrations. Power BI and semantic modelling. Data warehousing and analytics architecture. REST API integrations and SaaS connectivity. Git version control and CI/CD workflows. Advantageous Skills: Azure Data Factory, Synapse Analytics, or Databricks. Data quality, observability, and reconciliation frameworks. Experience with platforms such as Zendesk, HubSpot, Zoho, Intercom, or Aircall. Exposure to dbt or other modern analytics engineering tools. Apply now!
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