Srikanth Pandem

Architected and built scalable ingestion frameworks in Python (3.8–3.11) to extract, normalize, and load data from 50+ sources—including REST APIs (JWT/OAuth), SFTP, AWS S3, and databases—into GCS, powering downstream dbt and BigQuery pipelines. Engineered modular libraries for data extraction, schema validation, incremental/full-sync workflows, and robust error handling, standardizing patterns across pipelines. Owned the complete data lifecycle from ingestion to curated table publication, with on-call responsibility and SLA-driven incident resolution. Onboarded 3 critical data partners (Constellation, MRI, Realpage), implementing Hive-style partitioning for Constellation MLS APIs to manage over 1B rows daily from 8 endpoints, enabling performant full-loads for sales-deletion patterns. For MRI’s 80+ interdependent endpoints, designed a dynamic parameter builder to handle complex dependencies. Enhanced Realpage pipelines across 12 related endpoints. Built production-grade AI age

Led data engineering for lease-financing systems and monthly reporting. Designed SQL Server schemas, optimized stored procedures and queries, and implemented PD/LGD-based risk scoring logic. Built and maintained SSIS ETL packages and SSRS dashboards for financial reporting. Implemented Azure DevOps CI/CD pipelines for database deployments, documented data processes in Confluence, and performed performance tuning within SSMS. Collaborated with cross-functional teams in an Agile/JIRA environment.

Designed and implemented ADF pipelines ingesting data from S3, SQL databases, and REST APIs. Created raw, curated, and production databases in Azure SQL. Developed stored procedures and cleansing logic. Configured Azure Monitor dashboards, logs, alerts, and notifications. Implemented API-based verification flows (email, phone, address) and collaborated with business teams to model data.