Job description

• Design, develop, and maintain scalable data pipelines, ETL processes, and data lake solutions on Azure Data Platform. • Work with stakeholders including traders, risk analysts, compliance, and portfolio managers to understand data needs and deliver robust data solutions. • Build and manage data ingestion pipelines using Azure Data Factory, Databricks, Synapse Analytics, Event Hubs, Delta Lake, etc. • Develop and optimize SQL-based and PySpark-based ETL jobs for large-scale structured and unstructured financial datasets. • Implement data modeling, data warehousing, and data governance best practices aligned with financial industry standards. • Integrate market data feeds such as Bloomberg, Reuters, ICE, and internal trade data systems. • Ensure data security, lineage, monitoring, and auditing mechanisms are in place, adhering to regulatory and compliance needs. • Collaborate with DevOps teams to automate data workflows and deployment processes using tools like Azure DevOps, Terraform, Git. • Monitor and optimize data performance and cost usage of Azure resources. Required Skills & Qualifications: • 10+ years of experience in Azure-based data engineering. • Strong hands-on experience with Azure components: • Azure Data Factory • Azure Synapse Analytics • Azure Databricks • Azure Blob Storage / Data Lake Gen2 • Azure SQL / Cosmos DB / Event Hub • Proficient in SQL, Python, and PySpark for data transformation and analytics. • Sound understanding of investment banking processes, including trade lifecycle, risk and compliance, market data, equity/fixed income products, etc. • Experience working with large datasets, real-time data ingestion, and streaming architectures. • Familiarity with regulatory requirements like MiFID II, Basel, Dodd-Frank, etc., is a plus. • Good understanding of CI/CD practices and infrastructure as code for data projects. • Excellent communication skills and ability to work in a fast-paced, global financial environment.

Requirements