Architect and optimize data pipelines for ingesting and transforming large volumes of financial data using AWS services (Glue, Redshift, S3) to handle critical data workflows and ensure data accuracy.• Collaborate with compliance and legal teams to implement data governance and security frameworks that adhere to SEC and FINRA guidelines, using AWS KMS for encryption and IAM for strict access control policies.• Spearhead deployment of real-time streaming solutions using Apache Kafka and Spark Streaming, allowing faster transaction processing and enabling front-office systems to handle high-throughput, low-latency data needs.• Implement data quality validation within ETL workflows using SQL and Python, reducing data errors by 30% and improving the reliability of reports used by executive decision-makers and data analysts.• Design and maintain data models in Snowflake and Redshift, optimized for financial reporting, customer analytics, and risk assessment; reduced query times by 25% by refining table structures and indexing.• Lead initiatives to integrate machine learning capabilities into the data pipeline, supporting predictive analytics for customer behaviour and investment trend analysis, using AWS SageMaker for deployment.• Mentor junior engineers in best practices for data engineering within regulated environments, including data lifecycle management and GDPR compliance, ensuring consistent quality and security across projects.

Designed and implemented scalable ETL processes with Apache Airflow and Hadoop to manage high-frequency transaction data processing, providing stable and robust infrastructure for reporting and compliance.Developed and maintained credit risk and fraud detection pipelines using Spark and Kafka for real-time data processing, reducing fraud detection latency by 40% and enhancing risk mitigation capabilities.Integrated data anonymization and data masking techniques within data pipelines to ensure GDPR compliance and data security, leveraging AWS Glue for automated transformations and encryption tools.Built a data lake architecture on AWS to consolidate customer data from CRM systems, transaction data, and third-party sources, creating a single source of truth to enable comprehensive customer analytics.Optimized existing data warehousing solutions on Oracle and Snowflake, implementing table partitioning, clustering, and indexing strategies that improved query performance by 20% across the finance team.Partnered with finance and risk teams to develop customer 360° views for personalized marketing and risk modeling, integrating data across business units to support unified insights.Led a data engineering team in automating and scaling workflows using Python and SQL, reducing data preparation time by 50% and enabling faster reporting cycles for regulatory compliance.