Data Analyst – Databricks AI/BI & FinOps

Synopsis We are seeking an experienced, hands-on Data Analyst – Databricks to help build, optimize and enhance our modern data and analytics environment. The successful candidate will bring a strong combination of Databricks data engineering, Azure FinOps and cost optimization, data analysis, and visualization experience. You will work with large and complex datasets, develop scalable data pipelines, provide actionable insights, and collaborate closely with technical and business stakeholders to ensure data is accessible, reliable and cost-efficient. This is an opportunity for someone who can bridge data engineering and analytics, translating complex data into meaningful insights while helping optimize the performance and cost of our Azure and Databricks environment. Accountabilities Design, build and maintain scalable data pipelines and architectures within Databricks and Azure. Ingest, transform and analyze structured and unstructured data from multiple sources. Develop datasets and analytical solutions supporting business intelligence, reporting, analytics and data science. Analyze Azure and Databricks consumption and costs, identifying opportunities for FinOps optimization and improved resource efficiency. Develop dashboards and visualizations that translate complex datasets into clear, actionable business insights. Collaborate with business and technical stakeholders to understand requirements and deliver fit-for-purpose data solutions. Automate manual data processes and continuously improve data delivery, quality and platform performance. Support data governance, including data quality, cataloging, lineage and compliance. Monitor data pipelines and platform performance to ensure availability, reliability and scalability. Communicate technical findings and recommendations effectively to both technical and non-technical stakeholders.

Education & Experience 4+ years of relevant data engineering, data analytics or data platform experience. Hands-on experience with Databricks BI and visualization capabilities, particularly Databricks Genie (Candidates without direct Databricks Genie experience may also be considered where they demonstrate strong Databricks engineering, data analysis, visualization and Azure FinOps expertise, together with the ability to quickly develop Genie capability). Practical Azure FinOps and cloud cost optimization experience, including analyzing consumption, identifying cost drivers and recommending optimization opportunities. Strong experience with Microsoft Azure and Azure Databricks. Strong hands-on Databricks data engineering experience, including building and maintaining production data pipelines. Strong data analysis and visualization capability, with experience turning complex datasets into meaningful business insights. Strong Python and SQL skills for data engineering and analytical workloads. Experience developing and maintaining ETL/ELT pipelines and working with large, complex datasets. Good understanding of data warehousing, data modeling and data quality principles. Strong communication and stakeholder management skills, with the ability to translate technical information into clear business recommendations. Bachelor’s degree in Computer Science, Data Science, Engineering, Information Technology, or a related discipline.

Design, build and maintain scalable data pipelines and architectures within Databricks and Azure. Ingest, transform and analyze structured and unstructured data from multiple sources. Develop datasets and analytical solutions supporting business intelligence, reporting, analytics and data science. Analyze Azure and Databricks consumption and costs, identifying opportunities for FinOps optimization and improved resource efficiency. Develop dashboards and visualizations that translate complex datasets into clear, actionable business insights. Collaborate with business and technical stakeholders to understand requirements and deliver fit-for-purpose data solutions. Automate manual data processes and continuously improve data delivery, quality and platform performance. Support data governance, including data quality, cataloging, lineage and compliance. Monitor data pipelines and platform performance to ensure availability, reliability and scalability. Communicate technical findings and recommendations effectively to both technical and non-technical stakeholders.