Data engineering and warehouses
Python, SQL, BigQuery, Snowflake, Amazon Redshift and PostgreSQL. Pipelines, layered warehouse models, source integration, migration and data quality.
Expertise
Engineering capabilities across software, data and AI.
MV.tech combines application development, data platforms, analytics and AI integration. We use the tools your requirements call for and connect the implementation to the people, business rules and systems it needs to support.
Technical capabilities
Tools applied to the work in scope.
Business intelligence and analytics
Power BI, DAX, Tableau, Looker Studio, Streamlit and Plotly. Semantic models, reusable measures, dashboards and reconciliation.
Software and API development
TypeScript, JavaScript, Python, SQL and REST APIs. Business applications, customer portals, internal tools and system integrations.
AI applications and connectors
RAG, model APIs, embeddings, MCP servers and custom connectors. Knowledge search, document processing and AI integration into business tools.
Cloud delivery and automation
Google Cloud, AWS and Azure services, including Cloud Run, S3, Redshift and Azure Data Factory. Docker, GitHub Actions, scheduling and monitoring.
Blockchain data engineering
Python, Web3.py, contract ABIs and node RPCs. Transaction ingestion, event decoding, wallet reporting and multi-chain datasets.
Selected engineering experience
Experience in data platforms, reporting and AI.
Selected work from Meet Vaghasia’s individual engagements, including through talent platforms. Details are published on his Toptal profile.
Data platforms and AI · RootstockLabs
Redshift warehousing and ETL pipelines, with RAG integration for analytics and business reporting.
On-chain reporting · BCW Group
Multi-chain data consolidated in BigQuery, with Looker reporting for wallet activity and finance analysis.
AI evaluation · Turing
Gemini model evaluation and data preparation for a Google project delivered through Turing.
Credentials
Individual qualifications and professional profiles.
Google Cloud
Meet’s public profile lists the Professional Data Engineer certification.
IBM
The profile lists Big Data Engineer and Artificial Intelligence Analyst Mastery Awards.
Toptal
Meet is listed as a Verified Expert in Engineering on Toptal.
Engineering practice
The operating detail is part of the implementation.
A system needs to remain understandable after the first release.
- Data definitions and validation. Document transformations and business calculations, and compare important outputs with their sources.
- Access and deployment. Use scoped access, managed secrets and a reviewable deployment process appropriate to the project.
- Documentation and handover. Explain how the system is set up, how it runs and who owns operational decisions.
- Project-specific acceptance. Agree tests for the workflows, data or features being delivered, including relevant failure paths.
Contact MV.tech
Discuss the technical work your project needs.
Tell us what you want to build, improve or connect. We’ll discuss the requirements, the delivery options and the people the work needs.