I am Nitin Srivastava—a Principal Architect with 18+ years defining how enterprises define, govern, validate, and control data across its full lifecycle. From Operational Controls & Lineage to Data as a Product (DATSIS) and Modern Cloud Lakehouses.
Framework
Operational Data Controls & DATSIS
A technology-neutral approach to how modern enterprises define, control, reconstruct, and democratize trustworthy data across its full lifecycle.
Transforming static compliance policies into executable 10-point controls, automated reconciliations, threshold alarms, and auditable proof.
Architecting exact point-in-time state reconstruction, Change Data Capture (CDC), delta management, snapshots, and bi-temporal models.
Implementing data products adhering to the DATSIS standard: Discoverable, Addressable, Trustworthy, Self-describing, Interoperable, and Secure.
Designing canonical conceptual, logical, and physical schemas—from Kimball dimensional star models to Data Vault 2.0 and Lakehouse Delta designs.
Bridging technical column-level lineage with business lineage to explain exactly how business numbers are computed and what changes affect downstream systems.
Preparing enterprise data for reliable GenAI, LLMs, and AI agents. Eliminating hallucinations through semantic consistency, permissions, and ground truth.
Whether you need an architectural audit, corporate training, an executive hire, or bespoke consultation.
Real-world SaaS applications and developer utilities engineered to solve high-friction data modeling and engineering problems.
AI-first enterprise data modeling studio, schema relationship visualizer, and Databricks Delta Lake DDL generation engine.
Transform raw technical work history into structured, high-impact STAR behavioral interview answers and curated examples for leadership roles.
Calculate optimal driver/executor memory, CPU core allocations, and partition sizing to stop PySpark OOM errors.
Curated architectural reference models, dimensional schemas, and enterprise SQL query optimization playbooks.
I specialize in designing resilient, production-hardened data ecosystems where robust data modeling, Data as a Product, automated lineage, and governance-by-design replace brittle pipelines and data silos.
Across 18+ years, I have architected automated migration engines for 4,000+ database objects, engineered petabyte-scale data validation frameworks, accelerated analytics delivery by 60%, and saved global enterprises $1.2M+ in annual cloud compute waste.
Automated transformation from Teradata, Netezza, DB2, and SQL Server to modern cloud architectures.
Amazon Redshift, AWS Glue, EMR, S3, Databricks Delta Lake, and PySpark distributed workloads.
Domain-driven Data Mesh, semantic layers, and self-service analytics democratization platforms.
Unity Catalog, Collibra metadata, automated lineage, PyDeequ quality testing & schema enforcement.
Sharing production-grade engineering blueprints, architectural patterns, and video masterclasses with the global data community.
Watch 100+ masterclasses covering SQL execution plans, PySpark distributed optimizations, and cloud Lakehouse migration validation patterns.
Explore Masterclasses on YouTubeInteractive architectural guides for dimensional schemas, window functions, and query optimization.
Seeking enterprise advisory, corporate live training, an executive architecture role discussion, or bespoke consultation? Get in touch directly below.