Naresh Dulam
About
As the senior VP of Software Engineering at JPMorgan Chase, I oversee the design and implementation of enterprise-level Data Lakes/Mesh across various cloud platforms and technologies. With more than 14 years of experience in the software industry, I have a strong background in data engineering and architecture, software infrastructure and product management, and enterprise software development.
I am skilled in AWS and Azure services, Big Data technologies, programming languages, AI/ML frameworks, and tools. I work with cross-functional teams to optimize data architecture and storage solutions, resulting in cost-effective and scalable data ecosystems. I am also passionate about mentoring and sharing my knowledge and expertise with others and learning new technologies and trends in the data and cloud space. I aim to drive data-driven excellence and innovation in every project I undertake.
Java, Spark, Python, Airflow, K8s, Docker, Terraform, AWS, Azure, Lambda, Technology/services, Snowflake, Databricks, Immuta, starburst, Trino, Lang chain, Llamindex, Ollama, Linear regression, Random Forest, XGboost, SVM, Supervised Learning, Adaboost, XGBoost, LightGBM, Scikit-learn, PyTorch, TensorFlow
Skills & Expertise
AWS
Distributed Systems
Research Interests
datascience
Artificial Intelligence
Software Engineering
Data Engineering
Connect With Me
Experience
Vice President of Software Engineering
- Built WM Risk & Control Analytics is next gen data platform, Controls Data as Product from ground up to replace legacy data warehouse system, and Hadoop as part of modernization with seamless user experience for 1500+ business users. This helps the organization to reduce vendor license and consulting costs. With Data mesh architecture of New Data Analytics platform built on Data mesh principles reduce the storage cost, no duplication of data and with open-source cloud native technologies. o Envisioned, Architected, and operationalized the WM Data Analytics Hybrid Platform to run the MIS Reporting, data science and analytical usage for Private Bank. o Data lake with capacity of 1.5 PB supporting more than 2000 users on-prem (PB/IPB Data) and Cloud Data Lake for Market/Index data serving 200+ data scientists for AI/ML Use cases. o Designed and built a generic data ingestion pipeline framework, which helped in quicker data onboarding on to the platform. o Architected the design pattern for self-serve for both ingestion and consumption. This design pattern was made a standard for other technology teams, thus reducing build time for multiple teams across lines of businesses. JPMC Advanced Data Ecosystem is a firm wide public cloud -based data platform comprised of reusable components required by LOBs to establish and machine learnable datasets to the public cloud – making it available for AI/ML, analytics, BI Use cases. As part of cloud migrations all the app teams are required to build the cloud pipelines which is duplication of work. A framework required which adheres to JPMC security standards. o Architect/Develop the cloud pipelines for LOB to use the same pipelines in their cloud migration journey. o Responsible for building containerized applications for both data ingestion to cloud and transformation. Migrating the existing containerized application running in private cloud to public cloud AWS (Use case Airflow with OIDC Authentication)
Education
Jawaharlal Nehru Technological University Hyderabad
Role in Research Journals (2)
Reviewer
Reviewer
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