Specializing in multi-agent metadata lineage, continuous data validation frameworks, and high-velocity compute optimization. Building critical architectures that govern multi-terabyte flows and secure operational SLAs.
Moving away from manual documents that decay instantly. Lineage must be self-synthesized by parsing source operations directly, creating a dynamic graph model that prevents breaking changes before they hit production.
Data quality is not a post-hoc report; it is an active gatekeeper. By implementing real-time validation layers and reconciliation checks directly into pipeline runs, downstream metric drift is eliminated proactively.
Massive scale demands absolute warehouse discipline. Through intelligent partition pruning, schema optimization, and precise engine sizing, pipeline execution velocity can double while operational costs shrink dramatically.
Experience real-time interactive models demonstrating Shubham's core engineering architectures: automatic metadata lineage mapping and automated data validation safeguards.
This interactive graph represents a distributed metadata intelligence model. It automatically parses PySpark and SQL scripts from enterprise git repositories to map column and dataset relationships.
A Python-based framework executing thousands of automated assertions across processing streams daily. It performs schema drift monitoring, integrity checks, and metric reconciliation before writing to analytics-ready tables.
Click or hover on any structural layer in the pipeline topology to explore the underlying technologies and trace data flow integration.
Tableau, Custom Flask Dashboards, Experimentation metrics
Apache Airflow, Data Compass Lineage, Git APIs, Docker
Snowflake (Optimized), Databricks Delta Lake, AWS S3
Apache Spark, PySpark, Python Core, Parallelized Ingress
Multi-terabyte transaction logs, core risk stores, user registries
Building resilient cloud storage frameworks and robust curated schemas using Databricks and Snowflake. Focused on warehouse cost optimizations, multi-level partitioning, clustering, and strict staging-to-BI architectures.
Over 9 years of hands-on data architecting, evolving from ingestion automations to leading global metadata governance ecosystems at scale.
Architecting global data infrastructure, complex analytics models, and compliance lineage catalogs supporting 20M+ users and over 6M daily card transactions.
Consulted enterprise stakeholders to build performant reporting systems, design facial recognition-based attendance apps, and lead scalable technical workshops.
Automated legacy data preparation tasks and created scalable dashboard reporting workflows, reducing data preprocessing overhead.
Have an engineering challenge involving multi-terabyte processing, pipeline optimizations, or complex data quality automation? Let's connect.