Building scalable cloud, data and infrastructure systems — turning complex engineering problems into reliable, useful products.
I'm a Data Engineer working across cloud infrastructure, data platforms and data quality. My work sits at the intersection of reliable infrastructure and useful data — from AWS foundations and lakehouse architecture to governed pipelines, MDM and AI-ready systems.
I started with Electronics & Telecommunication Engineering and an Honors focus in AI/ML, then moved into enterprise data engineering. Today I'm focused on building systems that are scalable, observable and practical to operate.
I care about the part that happens after the demo: maintainability, data correctness, automation, observability and clear ownership.
Design cloud and data workflows with predictable failure handling, clear boundaries and operational simplicity.
Use infrastructure-as-code, reusable patterns and automation to remove unnecessary manual work.
Prioritize quality, lineage, governance and consistency so downstream teams can trust the data.
Balance performance, scale and cost — especially across distributed processing and large data volumes.
My experience spans the infrastructure underneath data systems as well as the engineering patterns that make those systems dependable in production.
AWS, Terraform and infrastructure patterns for enterprise data-quality workloads, with Databricks in the delivery stack.
↗Lakehouse architecture, Apache Iceberg, S3, PySpark and scalable data processing.
↗Ataccama Data Quality, MDM, Golden Record workflows and governed enterprise data.
↗Python, PySpark, SQL, APIs, ETL pipelines and automation focused on maintainable systems.
↗I started with an Honors foundation in AI/ML and keep building on it through hands-on projects, modern AI tooling and practical data-engineering use cases.
Honors in Artificial Intelligence & Machine Learning, backed by an engineering mindset around data, systems and experimentation.
Built an Image Fourier Transform application and explored LLM / RAG workflows — learning by turning AI concepts into working software and measurable engineering patterns.
I use AI as an engineering layer: connect reliable data to retrieval, LLM APIs and evaluation rather than treating AI as a black box. I stay current through certifications, documentation and small experiments before bringing a new technique into a project.
A timeline of my professional journey, learning, growing and building impactful data and cloud solutions.
View Full ResumeBuilding AWS infrastructure for Ataccama Data Quality for a leading U.S. banking organization, using AWS and Databricks across cloud infrastructure, data engineering, data quality and enterprise data governance.
Engineered scalable lakehouse platforms, ingestion frameworks, MDM/Golden Record solutions, APIs and governed ETL pipelines across regulated pharmaceutical data environments.
Electronics & Telecommunications Engineering with Honors in AI/ML. CGPA 9.34/10.
Certifications across data engineering, Databricks, cloud fundamentals and Generative AI — aligned with the systems I build in production.
Data Engineering
CertifiedGenerative AI · RAG · LLM applications
CertifiedCloud + Data
CertifiedAI / ML Foundations
CurriculumA mix of production engineering experience and hands-on projects — from enterprise lakehouse systems to developer tools and applied computing.
Scale, performance and reliability improvements from end-to-end data engineering work — from ingestion and migration to validation and APIs.
Independent projects that show how I think outside day-to-day enterprise work — visualization, distributed systems, developer tooling and applied mathematics.
Interactive table- and column-level SQL lineage with multi-dialect parsing, focus highlighting, query history, saved snippets and PNG export.
A high-performance Apache Spark cluster resource calculator designed to help Data Engineers reason about cluster sizing and configuration across YARN and Kubernetes.
A decentralized cloud-storage application combining Ethereum smart-contract infrastructure with IPFS-based storage, developed around a blockchain-first architecture.
The stack behind the systems I build — cloud, data, infrastructure and development tools, with their own visual language.
Terraform↗
PySpark↗
Apache Iceberg↗
Databricks↗
Python↗
SQL↗Spark↗
Docker↗
React↗
Git↗
Azure↗
Hadoop↗
Scala↗
Airflow↗
LangChain↗I'm always open to interesting engineering opportunities, collaborations or simply a good conversation.