• US Citizen

• Green Card

• EAD (OPT/CPT/GC/H4)

• Corp-Corp

• W2-Permanent

• W2-Contract

• Contract to Hire

• Consulting/Contract

• UG :- - Not Required

• PG :- - Not Required

• No of position :- ( 1 )

• Post :- 21st Mar 2025

With 18+ years of experience in big data architecture and cloud-based solutions, I have successfully led data transformation initiatives across finance, telecom, healthcare, retail, and automotive industries.

Core Expertise:

Databricks & Apache Spark: Architecting high-performance, scalable data solutions.

Cloud Technologies: Proven experience in AWS, Azure, and GCP, optimizing hybrid and multi-cloud environments.

AI & Machine Learning: Integrating AI/ML models, GenAI, NLP, and real-time analytics to enhance data-driven decision-making.

Data Engineering & ETL: Specializing in ETL/ELT, Data Vault 2.0, and Medallion architecture to streamline data pipelines.

Business Intelligence & Governance: Ensuring data quality, governance, and compliance while delivering insightful BI solutions with Power BI, Tableau, and Snowflake.

DevOps & Automation: Implementing CI/CD pipelines, Kubernetes, Terraform, and Jenkins to accelerate deployment cycles.

I have worked with BNY Mellon, Southwest Airlines, Comcast, and Vertex Pharmaceuticals, driving enterprise-wide data modernization with cutting-edge cloud-native technologies. My passion lies in leveraging data strategy, automation, and analytics to create future-proof, AI-driven solutions that empower businesses.

Strategic DE w/ Heavy Databricks/ BI / Data Architect

• in addition to the data engineering execution, needs abilities in solution architecture, informing technical architecture, Databricks experience, providing a point of view on best practices in databricks, ETL, etc.
• Collaborate with stakeholders to define data engineering requirements, success metrics, and alignment with organizational objectives.
• Design, develop, and deploy scalable, efficient, and robust data pipelines to support analytics, reporting, and AI applications.
• Create reusable frameworks and components to streamline data pipeline development and optimize workflows across multiple products.
• Shape technical direction and architectural decisions by providing expertise across teams, ensuring scalability and maintainability of data solutions.
• Work closely with DevOps, MLOps, and DataOps teams to ensure seamless integration of data solutions into operational environments.
• Regularly evaluate the performance of data pipelines, databases, and storage systems to identify areas for optimization and cost-efficiency.
• Refine and evolve data infrastructure to adapt to changing business requirements, ensuring reliability and efficiency.
• Mentor and coach junior team members, promoting technical growth and fostering a collaborative and innovative team culture.
• Facilitate knowledge sharing and drive the adoption of best practices for ETL development, coding standards, and data engineering workflows.
• Research and integrate cutting-edge tools, technologies, and methodologies to continuously advance the organization's data engineering capabilities.