Design, build, and optimize scalable batch, streaming, CDC, and event-driven data pipelines using modern data engineering best practices and production-grade Python and SQL.
Architect secure, resilient, high-performance data platforms and translate business and technical requirements into scalable cloud-based solutions.
Lead hands-on development and optimization of Databricks, Apache Spark, Delta Lake, and Lakehouse solutions, ensuring reliability, performance, and cost efficiency.
Design and implement Snowflake data engineering solutions, including ingestion, transformation, governance, orchestration, and secure data consumption patterns.
Engineer enterprise data platforms on AWS, leveraging cloud-native services to deliver secure, automated, observable, and highly available data solutions.
Develop reusable integration frameworks, APIs, CDC processes, and connectivity patterns across enterprise applications, SaaS platforms, governance tools, ERP, and CRM systems.
Build AI-ready data foundations, including RAG pipelines, vector retrieval, context engineering, and agentic workflows, enabling enterprise AI and GenAI use cases.
Drive engineering excellence through CI/CD, Infrastructure as Code, security-by-design, observability, governance, mentoring, and end-to-end ownership of data products and platform capabilities.