Cloud Data Platform & Analytics Warehouse

Built production ETL/ELT pipelines using Python, Airflow, and Docker to orchestrate ingestion, transformation, and delivery of terabyte-scale data. •Developed SQL-based data models and dbt-style transformations to power analytics dashboards and downstream reporting used by enterprise and municipal stakeholders. •Optimized warehouse performance through partitioning, clustering, and query tuning, reducing analytical query latency significantly for large datasets. •Built automated data quality frameworks — schema validation, freshness checks, anomaly detection — ensuring high trust in downstream reporting. •Designed orchestration workflows supporting automated ingestion, transformation, QA, and deployment across multiple operational environments. •Collaborated with domain experts to define data validation rules and metric definitions used across analytics deliverables.

Enterprise Analytics Data Pipeline

Designed distributed processing workflows leveraging Python, Spark, and cloud infrastructure to support millions of records daily. •Implemented automated preprocessing, feature engineering, and data enrichment workflows to support downstream analytics and modeling.•Developed SQL data models for reporting and business intelligence, including aggregation layers and business metric definitions. •Built data validation and quality assurance frameworks using precision, freshness, completeness, and accuracy metrics. •Built cloud-native data infrastructure on AWS and Azure for large-scale processing, monitoring, and orchestration. •Designed end-to-end analytics workflows covering ingestion, EDA, feature engineering, experiment tracking, and dashboard delivery. •Produced technical documentation, runbooks, and onboarding guides to support smooth handoff to analytics and engineering teams.

Enterprise Data Integration Platform

Built data ingestion services supporting large-scale production workloads. •Developed data processing pipelines for analytics, reporting, and predictive modeling use cases. •Implemented automated deployment, monitoring, and alerting workflows for production data pipelines.