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Mid-level, popular 'Data Engineer' title plus metro location and broad hiring demand increases applicant density.
Core data engineering skills are transferable, but banking-domain preference increases industry-specific fit requirements.
Explicit 6+ years requirement with mandatory Azure/Databricks delivery and pipeline experience makes filters strict.
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Design and build complex Azure Cloud-based data pipelines, data lakes, and data warehouses using Azure Data Factory, Azure Databricks, and Synapse Analytics.
Own delivery of enterprise-scale data transformation and modernisation programs aligned with organisation data strategy and vision.
Implement and optimise batch and streaming data processing pipelines, ensuring performance, cost control, and data quality monitoring frameworks.
6-10 years overall IT experience, with minimum 6 years in Azure Data Engineering delivery including development and deployment.
Proficient in Azure Data Factory, Azure Synapse Analytics, Azure Data Lake Storage Gen2, Azure Databricks (PySpark/Spark SQL), and SQL/T-SQL.
Experience with streaming technologies such as Kafka or Azure Event Hubs and batch-processing architectures; demonstrated expertise in Delta Lake and Lakehouse implementations.
Work Experience Required: 6-10 years in IT with significant Azure Data Engineering experience; Python, SQL, and Spark SQL proficiency mandatory.
Experienced in delivering end-to-end Azure data platforms for BI/ML consumption in fast-paced agile environments.
Skilled in cost optimisation and governance of cloud data platforms, with hands-on experience in DevOps workflows and CI/CD pipelines.
Familiarity with advanced data engineering concepts such as containerization, orchestration (Kubernetes) is advantageous but not mandatory.