Description

• Architect scalable, secure, high-performance data solutions for fraud detection and risk management.
• Design end-to-end data architectures including ingestion, transformation, storage, governance, lineage, and consumption layers.
• Build and optimize high-quality data pipelines (ETL/ELT) using Spark, PySpark, and Databricks.
• Handle structured and semi-structured high-scale datasets ensuring data integrity and quality.
• Work with data scientists to prepare training datasets, support feature engineering, and operationalize machine learning models.
• Implement cloud-native data solutions on Microsoft Azure using services like Data Lake, Synapse, Databricks, Event Hubs, Functions, and Key Vault.
• Establish data governance practices covering metadata, lineage, quality rules, and security controls.
• Drive technology evaluations, POCs, and contribute to the data platform roadmap.
  Lead deployments across environments ensuring smooth releases and minimal risk.
• Provide technical leadership, mentor data engineers, and maintain architectural standards across squads

Key Skills

Education

Bachelor's degree