I design and support secure, scalable cloud data platforms for analytics, reporting, machine learning, and generative AI workloads.

I have 6+ years of experience developing production data pipelines, building governed data layers, improving data quality, automating infrastructure and deployments, optimizing cloud costs, and supporting business-critical workloads in financial services and healthcare environments.

Python SQL PySpark Apache Spark Apache Airflow AWS Glue Azure Data Factory ETL/ELT

AWS Azure Databricks Snowflake Amazon Redshift PostgreSQL MySQL

Terraform Docker Kubernetes Helm GitHub Actions Jenkins ArgoCD

Prometheus Grafana CloudWatch Azure Monitor ELK

Data Quality Data Validation Reconciliation IAM/RBAC Encryption Audit Logging

A production-style financial transaction pipeline implementing bronze, silver, quarantine, and gold data layers.

Technologies: Python, PySpark, Apache Airflow, SQL, PostgreSQL, Docker and GitHub Actions.

Project highlights:

• Bronze, silver, quarantine, and gold data layers
• Automated data-quality validation
• Invalid-record quarantine with error reasons
• Source-to-target reconciliation
• Airflow-based orchestration
• SQL reporting models
• Automated testing and GitHub Actions CI/CD
• 250 synthetic source records processed
• 238 valid records and 12 quarantined records
• 95.2% data-quality pass rate

View the project repository →

This project uses synthetic data and contains no employer code, client data, credentials, or proprietary architecture.

A production-style synthetic healthcare claims pipeline implementing bronze, silver, quarantine, and gold lakehouse layers.

Technologies: Python, PySpark, Apache Airflow, SQL, Docker and GitHub Actions.

Project highlights:

• Synthetic healthcare claims ingestion
• Duplicate and invalid-claim detection
• Patient-ID pseudonymization
• Raw patient IDs and names removed from the curated silver layer
• Invalid-record quarantine with error reasons
• Provider, diagnosis, and monthly-utilization analytics
• Source-to-target reconciliation
• Automated testing and GitHub Actions CI/CD
• 300 synthetic claims processed
• 284 valid masked claims and 16 quarantined claims
• 94.67% data-quality pass rate

View the project repository →

All patient, provider, diagnosis, and claim information is synthetic. The project contains no employer code, client data, credentials, or proprietary architecture.

• AWS Certified Solutions Architect – Associate
• Microsoft Certified: Azure Administrator Associate
• HashiCorp Certified: Terraform Associate
• Databricks Academy Accreditation – Generative AI Fundamentals

My current areas of technical development include:

• Cloud data-platform observability
• Infrastructure as Code for data workloads
• Pipeline reliability and operational monitoring
• Governed datasets for analytics and AI/ML workloads
• Secure deployment automation with Kubernetes and CI/CD

• Portfolio: sandya-beemaneni.vercel.app
• LinkedIn: Sandya Beemaneni
• Email: bsandya1926@gmail.com

All public projects use synthetic or publicly available data and are independent reference implementations.