Azure Data Engineer

Responsibilities

• Design, develop, and maintain robust and scalable data pipelines for ingesting, transforming, and loading data from various sources (e.g., APIs, databases, financial data providers) into the Azure Databricks platform.
• Optimise data pipelines for performance, efficiency, and cost-effectiveness.
• Implement data quality checks and validation rules within data pipelines.
• Implement complex data transformations using Spark (PySpark or Scala) and other; develop and maintain data processing logic for cleaning, enriching, and aggregating data.
• Ensure data consistency and accuracy throughout the data lifecycle.
• Work extensively with Azure Databricks Unity Catalog, including Delta Lake, Spark SQL, and other relevant services.
• Implement best practices for Databricks development and deployment.
• Optimise Databricks workloads for performance and cost.
• Integrate data from various sources, including relational databases, APIs, and streaming.
• Implement data integration patterns and best practices.
• Work with API developers to ensure seamless data exchange.

Data Quality & Governance

• Hands on experience to use Azure Purview for data quality and data governance.
• Implement data quality monitoring and alerting processes.
• Work with data governance teams to ensure compliance with data governance policies and standards.
• Implement data lineage tracking and metadata management processes.
• Collaborate closely with data scientists, economists, and other technical teams to understand data requirements and translate them into technical solutions.
• Communicate technical concepts effectively to both technical and non-technical.
• Participate in code reviews and knowledge sharing sessions.
• Implement automation for data pipeline deployments and other data engineering tasks.
• Work with DevOps teams to implement and build CI/CD pipelines for environmental deployments.
• Promote and implement DevOps best practices.

Qualifications

• 10+ years of experience in data engineering, with at least 3+ years of hands‑on experience with
• Strong proficiency in Python and Spark (PySpark) or Scala.
• Deep understanding of data warehousing principles, data modelling techniques, and data integration patterns.
• Extensive experience with Azure data services, including Azure Data Factory, Azure Blob Storage,
• Experience working with large datasets and complex data pipelines.
• Experience with data architecture design and data pipeline optimization.
• Proven expertise with Databricks, including hands‑on implementation experience and certifications.
• Experience with SQL and NoSQL databases.
• Experience with data quality and data governance processes.
• Experience with version control systems (e.g., Git).
• Experience with Agile development methodologies.
• Excellent communication, interpersonal, and problem-solving skills.
• Experience with streaming data technologies (e.g., Kafka, Azure Event Hubs).
• Experience with data visualisation tools (e.g., Tableau, Power BI).
• Experience with DevOps tools and practices (e.g., Azure DevOps, Jenkins, Docker, Kubernetes).
• Experience working in a financial services or economic data environment.
• Azure certifications related to data engineering (e.g., Azure Data Engineer Associate).

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