DEEP PATEL

Part of Finance One F1, a strategic big data platform for the entire CFO business, developed in-house by the FL Engineering team.

• Managed data accuracy, data completeness, data transformation, and validation across the managed data layer for SWISS and NY.
• Implemented Jenkins pipelines to automate the build, test, and deployment processes, ensuring seamless and efficient CI/CD workflows.
• Automated routine tasks using scripting languages such as Shell and Python, reducing manual intervention and operational overhead.
• Resolved data integrity and quality issues in SIT/UAT/Production, addressing data duplication and missing columns in Parquet/JSON (L2/L3 Support)
• Enhanced 250+ feeds batch processing stability and performance in Managed Data Layer using Python, Spark, Shell scripting, and CTRLM Job Scheduler.
• Hands-on experience in ETL/ELT processes involving the transformation of source data as per technical and business rules to comply with user requirements and regulatory standards.
• Familiar in utilizing REST API, Docker, Containers, and the Kubernetes framework for improved data access and utilization.
• Contributed in multiple releases in F1 across different work streams.
• Implemented Data Warehouses, Big Data, and Data Lakes with a strong understanding of Master Data Management and DQ checks.
• Built mappers and APIs to enable cross-cluster dependency and user access to data.
• Developed Hive tables, views, mappers, and Linux scripts to process incoming reference data from multiple data hubs.
• Played a key SME role in data migration and decommissioned multiple job/feeds activities during the Parent Bank Merger phase with UBS, reducing operational costs.
• Integrated Continuous Integration/Deployment pipelines, code reviews, pull requests, unit/integration/e2e testing.
• Currently gaining cloud experience with UBS on Azure and Databricks, optimizing workload patterns to achieve a 25% decrease in computational expenses.
• Optimized data partitioning with PySpark, enhancing Spark job performance in Databricks workspace and achieving a 7% increase in processing efficiency and faster query execution.

• Assisted in the migration project of Teradata Enterprise Data Warehouse to Google Cloud Platform, implementing techniques such as Cloud Composer and Airflow DAGs.
• Identified and resolved issues, deploying interfaces for a smoother transition process
• Acquired hands-on experience with various GCP Components such as Big Query, Cloud Storage, Cloud Composer, and Dataflow.