Senior Data Engineer (Databricks on Azure)

We are looking for a Senior Data Engineer with advanced, hands-on Databricks on Azure experience to design, build, modernise and operate scalable data pipelines supporting Tax technology platforms. You will migrate and enhance existing Spark and Python workloads on Azure Databricks and build scalable ingestion and document-processing capabilities. We need someone who has owned production Databricks on Azure pipelines end to end, understands the wider system, surfaces assumptions and trade-offs, and proposes better approaches.

• →Design, build, and support scalable data pipelines using Python, Apache Spark, PySpark, and Databricks on Azure.
• →Modernise and migrate legacy data processing workloads to secure, cloud-native platforms.
• →Build and maintain batch data ingestion pipelines from structured and unstructured sources.
• →Integrate data from REST APIs, SharePoint, document repositories, enterprise applications, and cloud platforms.
• →Implement data quality, monitoring, observability, and operational controls.
• →Optimise data workloads for performance, scalability, reliability, and cost efficiency.
• →Develop document extraction, classification, metadata enrichment, and automation pipelines.
• →Apply software engineering practices, including Git, CI/CD, automated testing, and code reviews.
• →Build, deploy, troubleshoot, and maintain production ETL/ELT pipelines.
• →Collaborate with architects, developers, tax subject matter experts, and platform teams.
• →Analyse existing codebases and identify opportunities to improve maintainability, security, and reliability.
• →Use AI-assisted development tools to build, review, test, and maintain data pipeline code.

• 5+ years of experience in data engineering.
• Strong Python development experience.
• Strong Apache Spark and PySpark experience.
• Advanced, hands-on Databricks on Azure experience in production (Delta Lake, Unity Catalogue, Workflows/Jobs, ADLS Gen2, Azure Data Factory).
• Strong SQL and data modelling skills.
• Experience building, testing, deploying, and troubleshooting production ETL/ELT pipelines.
• Experience with Git-based source control and CI/CD pipelines (e.g., Azure DevOps).
• Experience consuming REST APIs for data ingestion, including authentication, pagination, and failure handling.
• Experience writing automated tests for data pipelines.
• Strong understanding of data quality, reliability, and data engineering best practices.
• Strong problem-solving, communication, and collaboration skills, with sound judgement to analyse unfamiliar codebases, identify gaps and trade-offs, challenge requirements and propose practical improvements.

• Experience modernising legacy data pipelines.
• Experience with document processing, intelligent document extraction, or unstructured data.
• Familiarity with AI-assisted development tools such as Codex, GitHub Copilot, or similar.
• Experience with streaming data pipelines or large-scale data repositories.