Ravi Ragampeta

• Built and led a 0→1 engineering organization of nine, owning delivery and reliability of both existing mobile and newly built asset management, enterprise data, and trading/research platforms.
• Delivered a Full-stack Asset Management Platform in eight months, providing real-time and historical insights into DA/RT revenue, prices, volumes, outages, and asset metrics; accelerated asset digitization and onboarding of new capacity.
• Led enterprise data platform modernization, migrating 40% of legacy pipelines to Snowflake using an event-driven, medallion architecture for ingestion and Bus Architecture principles for reusing domains—reducing costs by 20% and accelerating cross-domain analytics and asset onboarding.
• Leading the design and delivery of the firm’s next-generation Trading Platform, including a Research, Trade Capture, and PnL system for structured utility deals representing 30–40% of firm revenue, replacing fragmented Excel workflows with governed, automated, enterprise-grade solutions.
• Designed and implemented a domain-driven API framework with Redis caching, delivering low-latency pricing, volume, revenue, and market datasets to internal and external consumers through a standardized serving layer.
• Defined and led implementation of enterprise OAuth2-based entitlement controls for UI and API access, aligning security, audit, and compliance requirements without slowing product delivery.
• Established Datadog-based observability across all containerized workloads, improving system uptime, latency tracking, and compliance reporting.
• Defined and socialized the firm-wide Data and AI platform architecture with the CEO and CIO, securing cross-functional buy-in and adoption across Trading, Asset Management, Risk, and Operations.

Tech: Python/FastAPI, React/TypeScript, SQL Server, Databricks, Snowflake, Azure(EventGrid, APIM, Containers, ADF, ADLS), Redis, Docker, Datadog

• Led a global team of 7 developers + 2 automation engineers (and 15 contractors) to deliver ATLAS, a firm-wide Accounting & Non-Accounting Transaction Lineage Platform integrating data orchestration, enrichment, rules, and traceability.
• Consolidated 70+ legacy feeds and 10 stand-alone apps into ATLAS, creating a low-code, microservices framework that cut new-feed setup time by 60 % and eliminated redundant pipelines.
• Made ATLAS the single point of entry into the accounting system, providing full transaction lineage from OMS to custodian with self-service break triage (split/expense/income).
• Partnered with Operations & Trading to onboard diverse datasets (Spot FX, cash adjustments, subscriptions/redemptions, corporate actions, recon breaks, and reference data).
• Oversaw a portfolio of ~20 operational apps for cash projections, non-executed trade management, and recon exception resolution, mentoring leads and aligning SLOs with business partners.
• Initiated technology-modernization and cost-optimization programs (AWS rightsizing, Java/Python/React upgrades, Postgres → Oracle migration) and launched a Snowflake POC for research analytics.
• Strengthened platform security & resilience: implemented JFrog X-Ray scanning, password rotation, and region/AZ failover testing with Infrastructure and PwC audit readiness.

Tech: Java/Spring Boot, Python, AWS (EC2, S3, Step Functions, Lambda, RDS, API Gateway, Eventbridge), Kafka, Redis, Oracle/Postgres/Snowflake

• Led a 10-member engineering team (onshore + offshore) to implement Charles River IMS (CRIMS) on Microsoft Azure for a $3.9T AUM global asset manager, replacing BlackRock Aladdin for Fixed Income Active and OCIO businesses managing $780B.
• Delivered CRIMS for a $300B AUM client within one year, replacing Bloomberg TOMS and saving the client $3.5M in renewal costs through timely, production-grade delivery.
• Implemented a 24×6 multi-region ingestion pipeline for pre-trade data delivery (funds, securities, benchmarks, positions, analytics) across Bloomberg, MSCI, MarkitPartner, ICE, and six ABORs, applying golden-copy and security resolution rules for consistent, validated data across platforms.
• Designed cross-asset security and fund trading workflows for multiple asset classes with support for multi-allocation/tax lots using Kafka and routing rules to improve the efficiency of trade execution and order management and ensure proper recording of daily orders in accounting systems (ABOR).
• Developed a flexible messaging framework between CRIMS and ABOR that allowed adding/removing attributes without code changes, accelerating time to market and simplifying onboarding of new instruments.
• Built a rules-based pricing hierarchy engine (SQL) to enrich securities with prices and analytics from multiple sources; enabled dynamic ranking by security type, data availability, and day-type priority.
• Automated reconciliation across six accounting systems by building dashboards, SQL-driven quality checks, and notifications; increased market-value match rate from ~40 % to 95 %, reducing trading-day breaks and improving Task Library adoption.

Tech: Java, Python, IBM MQ, Kafka, Azure, SQL Server, CRIMS SDK

• Spearheaded launch of Case Management System and Machine Learning component to predict medication non-adherence of patients for specialty pharmacy support services for customer.
• Managed integration of UX, Engineering, and Machine Learning teams to deliver aforementioned components in aggressive timelines that helped customer process business for risk stratification with pharmacy and drug manufacturer.
• Performed data analysis, cleansing, and transformations for all datasets using Python and SQL on top of Spark/Hadoop Data Lake; Created training sets that sourced machine learning models and validated model against ground truth.

• Re-architected the Trade Selection platform, improving throughput by 300 % and enabling traders to hedge counterparty risk daily instead of weekly, directly reducing exposure latency.
• Migrated complex business logic from PL/SQL to C#, introducing server-side caching (Enterprise Library) and singleton-based preload of trades and reference data to enhance scalability and responsiveness.
• Leveraged Oracle global temporary tables for enabling parallel processing and partitioning with local/global indexes, significantly improving parallel trade selection processing.
• Designed and delivered the Flow feature, a dynamic, GUI-driven trade filtering framework allowing users to build on-the-fly risk queries using 100+ trade attributes and logical operators (=, !=, >, <, IN, NOT IN, etc.) on a dataset of over 5 million trades.
• Developed a Static Data ETL pipeline using a Pipes and Filters pattern with Concurrent Collections, streamlining ingestion and transformation from multiple upstream systems.
• Applied multithreading and async programming to optimize data load and computation, achieving major gains in performance and stability under peak trading volumes.
• Elevated code quality and maintainability by instituting unit testing with NUnit and FakeItEasy, embedding test coverage as a delivery standard.

Tech: C#, .NET, Oracle PL/SQL, Enterprise Library Caching, Multithreading, Async/Await, NUnit, FakeItEasy

• Led development for the Claims module in a Federal–State Unemployment Insurance modernization program, replacing legacy COBOL systems with a scalable .NET-based platform.
• Integrated state and federal systems (DHS, DMV, DOH, SSA) to enable secure, real-time identity and eligibility verification.
• Architected automated correspondence generation using HP Exstream, streamlining claimant communications and reducing manual processing.

• Developed an in-house content and microsite workflow for 4,000+ franchisees, improving turnaround by 95% and saving over $1M in outsourced costs.
• Built WCF-based services for real-time search to show the nearest Tax School based on Zip Code/OfficeID/ClassID registration, and payments (Authorize.Net), enhancing the customer experience.
• Implemented and administered Ektron CMS 400.Net, enabling scalable publishing and marketing automation across U.S. and Canadian sites.