Suraj Singh

(Strategic Enterprise Transformation Program)

• Owned end-to-end data and solution architecture for one of Gartner's highest-priority enterprise modernization programs, defining target architecture, integration patterns, data flows and technical standards across Annaplan, Master Data Management, Order Management, Product Catalog and Data warehouse. .
• Architected a cloud-native enterprise data platform on Databricks, AWS, PySpark, Kafka and Spark Structured Streaming, supporting batch and near-real-time data processing, CDC, incremental processing and fault-tolerant data pipelines.
• Defined integration and data-processing patterns across five enterprise source systems, establishing a scalable architecture and enterprise standard for licensed-user-based alignment.
• Partnered with Product Management, Salesforce Engineering, MDM, Order Management, Anaplan Team, Platform Engineering and business stakeholders to translate business requirements into scalable data architecture and solution designs.
• Designed enterprise-wide data quality, validation, reconciliation, monitoring and observability frameworks, improving data reliability and trust across the end-to-end data lifecycle.
• Designed data pipelines delivering trusted datasets to Amazon S3 and the Enterprise Data Warehouse, supporting seven downstream applications and more than 5,000 internal users across Sales, Services, Revenue Operations, Finance and Analytics.
• Reduced business data availability from 2–3 days to less than 5 hours, enabling near-real-time licensed-user alignment and improving business responsiveness by more than 90%.
• Led architecture and technical design reviews, engineering standards, sprint planning, production releases and technical risk management while mentoring a team of six data engineers.
• Presented technical strategy, architecture decisions, delivery risks and modernization recommendations to Gartner's CTO organization, GVPs, EVPs and cross-functional business stakeholders.
• Led Agile execution across Product, Engineering, MDM and business teams, aligning technical roadmaps and delivery milestones with enterprise transformation objectives.

• Designed and developed an end-to-end RAG architecture for a Legal AI Assistant, covering document ingestion, metadata extraction, vectorization, retrieval, ranking and LLM-based grounded response generation.
• Designed a hybrid retrieval architecture combining vector similarity and set-based similarity techniques to improve retrieval relevance beyond standard vector-search approaches.
• Developed a custom weighted ranking algorithm to optimize Top-K document retrieval precision.
• Implemented metadata-aware retrieval and context-grounding pipelines to improve response accuracy and reduce hallucination risk.
• Designed the solution with clear separation between data ingestion, retrieval, ranking and LLM response-generation components, enabling independent optimization of the data and AI layers.

• Architected and scaled ETL/ELT pipelines for both structured and unstructured enterprise data across multiple source systems.
• Designed and implemented data quality frameworks including validation, reconciliation, and monitoring to ensure accuracy, consistency, and trustworthiness.
• Built AWS-based distributed data platforms, leveraging managed services like AWS Glue, AWS Athena , AWS Lambda, SQS, Eventbridge for scalability, reliability, and cost efficiency.
• Developed data pipelines using PySpark , AWS Glue to support low-latency ingestion and processing use cases.

• Designed and implemented enterprise data warehouse and data onboarding architectures** supporting investment-management data, analytics and reporting use cases.
• Architected scalable ETL/ELT frameworks and data integration solutions across multiple enterprise data sources, with emphasis on performance, reliability and maintainability.
• Developed performance-optimized SQL/PLSQL and Python-based data processing frameworks for high-volume enterprise datasets.
• Designed AWS-based data ingestion architectures supporting analytical and reporting workloads at scale.
• Implemented data quality utilities and validation frameworks to improve accuracy, consistency and trustworthiness of enterprise data.
• Conducted technical design reviews across engineering teams and provided guidance on architecture, data processing patterns, performance optimization and engineering best practices.

• Designed enterprise data integration and onboarding architecture across multiple Oracle systems, implementing scalable SQL/PLSQL/Python ETL pipelines and data models supporting analytics and reporting.
• Built scalable data ingestion and transformation pipelines using SQL, PL/SQL, and Python, following performance and reliability best practices.
• Implemented data models and transformation logic to support large-scale analytics and reporting use cases.
• Developed analytical cubes and dashboards, enabling operational and business insights across global Oracle locations.
• Worked on platforms processing high-volume access and badge data, ensuring accuracy, consistency, and timely availability.

• Built Data warehousing and capacity planning solutions for large telecom clients, supporting infrastructure planning and utilization forecasting.
• Designed and implemented ETL pipelines using SQL and PLSQL to collect, process, and summarize infrastructure metrics from VMware, OpenStack, legacy servers, and applications.
• Aggregated and rolled-up data across multiple dimensions (environment, platform, hypervisor, application) to support weekly and monthly capacity analysis.