Banking Data Engineering & Governance
A conceptual architecture based on banking-domain experience: enterprise sources, GCS ingestion, Dataflow processing, BigQuery staging and curated analytics.
View detailsSARAVANA KUMAR / PROJECTS
Real employer experience is separated from future demonstration ideas — no fabricated performance metrics.
THE PORTFOLIO
Enterprise experience is clearly distinguished from proposed Agentic AI demonstrations. No fictional project results.
A conceptual architecture based on banking-domain experience: enterprise sources, GCS ingestion, Dataflow processing, BigQuery staging and curated analytics.
View detailsEleven years in enterprise IT. Specific project ownership, client assignments and achievements are reserved until verified.
View detailsA proposed AI agent that reviews sample data checks and recommends human-approved corrections. Not a completed product.
View detailsA proposed read-only assistant for investigating sanitized pipeline logs and suggesting safe diagnostic steps.
View detailsEXPERIENCE-BASED CASE STUDY
Conceptualized from described banking-domain data workflows, not a public client architecture or claim of sole ownership.
Scheduling and monitoring with Cloud Composer; data quality validation, duplicate checks, reconciliations and permission controls across the workflow.
AI INNOVATION LAB
Concept: assess quality failures, propose checks and require approval for changes.
Not yet deliveredConcept: read sanitized logs, identify likely causes and guide safe triage.
Not yet deliveredConcept: personalize practice questions and explain areas to improve.
Not yet deliveredWORK WITH ME
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