Chaitanya Vullaganti

Enterprise Cash Flow Forecasting & Financial Operations Platform

Designed and delivered an end-to-end cash flow forecasting and financial operations platform on Palantir Foundry for treasury and FP&A — governed Bronze/Silver/Gold pipelines, a semantic finance Ontology, ML-based monthly forecasting, and operational applications including an OSDK-powered React app.

• Modeled a finance ontology spanning CashTransaction, GLEntry, CashFlowForecast, MonthlyCashFlowStatement, and ReconciliationBreak, with link types connecting transactions to accounts, forecasts, and exceptions.
• Designed a Bronze, Silver, and Gold data architecture on Foundry following lakehouse principles across ingestion, curation, ML, Ontology, and application layers.
• Built ingestion pipelines for bank transactions, GL entries, chart of accounts, and master reference data using Pipeline Builder and Code Repositories.
• Developed reusable Python, PySpark, and SQL transforms for cleansing, enrichment, reconciliation, and financial reporting with readable lineage.
• Tuned PySpark jobs, partitions, and joins against production behavior, cutting pipeline runtimes and stabilizing month-end workloads.
• Implemented data quality expectations, schema validation, and reconciliation logic to surface bank vs. GL breaks before they reached finance users.
• Configured governed Action Types for break assignment, resolution capture, forecast overrides, and approvals — with object-level permissions for treasury roles.
• Developed monthly cash flow forecasting models on historical ledger and bank activity, publishing forecasts with confidence intervals into the Ontology.
• Delivered Workshop apps — CFO Cash Dashboard, Forecast vs Actuals, Line Item Explorer, and Reconciliation Workbench — for treasury and FP&A.
• Built a React and TypeScript application on the Ontology SDK (OSDK) for workflows outside Workshop, secured with OAuth-enabled Foundry third-party apps.

Collections Management System — Real-Time, Event-Driven Platform on Foundry

Replaced a legacy Oracle BRM batch collections engine for 921K billing accounts and 19.7M invoice records, migrating from daily batch eligibility to event-driven real-time evaluation — cutting time-to-action from 24 hours to near-zero.

• Architected CDC and batch merge pipelines across six Oracle BRM tables using LEFT ANTI JOIN merge with timestamp-based deduplication.
• Designed an eight-object-type ontology spanning transform-backed and action-managed objects with full link-type relationships.
• Implemented a dual state machine — collection record (5 states, 10 transitions) and collection action (7 states, 12 transitions) with cascading side effects.
• Defined an eligibility rules engine evaluating five static filters plus aggregation thresholds across 962 profile-action mappings.
• Authored 15+ production TypeScript Ontology Functions covering eligibility, record creation, action scheduling, Mailgun email, and CSR overrides.
• Configured Automate monitors for real-time entry and full lifecycle automation with zero manual intervention on standard paths.
• Delivered the initial production sweep creating 1,917 collection records and 26,749 collection actions from standing overdue balances.

AML Alert Manager & Case Manager — Anti-Money Laundering Workflows

Delivered Foundry applications for AML alert triage, investigations, SAR preparation, and FINCEN filing — with governed Ontology entities, automated case creation, and full auditability.

• Built ingestion pipelines to onboard historical and daily transactional data from legacy systems into Foundry for downstream compliance teams.
• Designed Pipeline Builder transforms and loaded curated datasets into the Ontology to standardize compliance entities and relationships.
• Implemented business rules enabling automated alert triage and case creation while preserving auditability and traceability.
• Developed Workshop apps for alert review, investigations, SAR preparation, and FINCEN feedback/rework inside Case Manager.
• Led demos with compliance SMEs; documented designs, runbooks, and onboarding guides; mentored engineers on Foundry best practices.

Real-Time Operations Data Platform — Streaming, Ontology & Workshop

Developed and optimized real-time streaming and batch pipelines in Foundry, governed Ontology models for devices/circuits/outages, and Workshop applications — including a Databricks compute-pushdown proof of concept.

• Monitored pipeline health dashboards and resolved warnings, late data, and schema changes to maintain high availability.
• Scaled compute and tuned partitions for growing volumes; managed Git branching, PRs, and release schedules for auditable deployments.
• Built cleansing in Pipeline Builder (deduplication, imputation, unit translation, schema alignment); enhanced correlation models to improve incident detection.
• Created Ontology Object/Action/Link Types with RBAC; delivered Workshop apps and Quiver/Contour dashboards for outage maps and operational KPIs.
• Integrated Databricks Delta/Iceberg via Foundry virtual datasets with compute pushdown; benchmarked Photon, serverless, and Pipeline Builder for compute strategy.

Enterprise CRM on Foundry — Ontology, Pipeline Builder, Workshop, Automate & AIP

Built a comprehensive CRM using Code Workspaces, Ontology Manager, Workshop, Pipeline Builder, Automate, and Vertex — with AIP/LLM synthetic data and TypeScript auto-assignment of employees to clients by state and industry.

• Built automated Pipeline Builder ETL for ingestion, transformation, and loading across CRM components with Python/SQL data quality work.
• Created Ontology Object Sets and Action Types; implemented AIP Logic / LLM synthetic data and specialized table splits.
• Developed TypeScript Automate functions for auto-assigning employees to new clients based on state and industry.
• Designed Workshop UI with visualizations, filters, custom widgets, overlays, variables, functions, and real-time metrics.

Healthcare Data Intelligence & Predictive Analytics Platform

Built predictive analytics and machine learning solutions supporting healthcare operations, risk assessment, and business intelligence — end-to-end workflows from data prep through modeling, NLP, and interactive reporting.

• Delivered predictive models for risk assessment, population segmentation, and operational forecasting across structured and unstructured datasets.
• Built end-to-end modeling workflows covering preparation, feature engineering, encoding, and class balancing as modular R scripts.
• Applied supervised and unsupervised techniques — regression, tree ensembles, SVM, clustering — selected against precision, recall, ROC-AUC, and cross-validation.
• Built NLP workflows for free-text feedback analysis; developed Tableau dashboards for leadership KPIs.

Enterprise Business Intelligence & Reporting Platform

Built enterprise BI and reporting solutions on large-scale transactional data — SQL analytical datasets, interactive Tableau dashboards, and KPI reporting for performance monitoring and decision support.

• Designed reusable analytical data models and SQL reporting datasets supporting executive KPI reporting and operational analytics.
• Wrote complex SQL with CTEs, window functions, ranking, subqueries, and aggregations to turn transactional data into business-ready datasets.
• Built interactive Tableau dashboards with calculated fields, parameters, drill-down, and dynamic filters; automated recurring reporting.