Data Engineer Real-Time

Sravan Kumar Reddy Ramidi

Data Engineer

PHONE: 316-***-**** EMAIL: **************@*****.*** LinkedIn

SUMMARY

Proactive and detail-oriented Data Engineer with over 5 years of experience designing and optimizing scalable data solutions on Azure and AWS. Adept at architecting end-to-end ETL pipelines, big data processing, and cloud-native data infrastructure using tools like ADF, Databricks, Synapse, Glue, Redshift, and PySpark. A collaborative problem-solver with a track record of enhancing operational efficiency, reducing costs, and driving data-driven decision-making. Passionate about building resilient, high-performance data ecosystems that empower businesses to extract actionable insights

EXPERIENCE

CVS Health, Boston, MA

02/2023 – Present

Data Engineer

Developed a robust pipeline for ingesting and processing unstructured data (XML, JSON, PDFs, documents, images) from Box using Azure Blob Storage and Azure Cognitive Services, preserving file formats and metadata.

Automated ingestion of unstructured data from Box, improving operational efficiency by 40%.

Designed and implemented scalable data pipelines using Azure Databricks, reducing claims processing time by 40% and scaling to handle 10x data spikes.

Built a real-time fraud detection system using Apache Kafka, achieving 95% accuracy and saving $1M+ annually in fraudulent payouts.

Created a centralized data lake using Azure Data Lake Storage (ADLS), supporting petabyte-scale claims data with 99.99% durability and cost efficiency.

Automated ETL workflows using Azure Data Factory, improving ingestion speed by 35% and reducing manual effort by 50%.

Developed Power BI dashboards integrated with Snowflake for real-time analysis of claims trends, enabling sub-second query responses.

Enabled real-time analytics using Azure Stream Analytics, reducing decision-making latency by 60%.

Implemented secure role-based access control with Azure RBAC and Apache Ranger to maintain GDPR and HIPAA compliance.

Orchestrated complex data workflows using Dagster, maintaining 99.9% reliability across 50+ daily batch jobs and reducing failures by 25%.

Integrated ML models for fraud prediction using Azure Machine Learning, increasing fraud detection accuracy by 30%.

Reduced infrastructure costs by 25% through Azure Cost Management, reserved instances, and auto-scaling strategies.

Enabled automated customer notifications via Azure Logic Apps and Service Bus, reducing support calls by 20%.

Migrated legacy claims data to Snowflake, improving reporting speed by 60%.

Monitored pipelines with Azure Monitor and Grafana, achieving zero downtime and 99.9% reliability.

Conducted a PoC using Apache Kafka for real-time claims processing, doubling throughput compared to traditional batch processing.

Cognizant, India

03/2021 – 02/2022

Data Engineer

Architected an end-to-end inventory optimization solution using Azure Databricks and IoT data ingestion via Azure Event Hubs, reducing stockouts by 20%.

Developed real-time inventory pipelines using Azure Event Hubs and Azure Stream Analytics, ingesting IoT and structured data from Azure SQL Database, enabling near-instant stock visibility and reducing discrepancies by 30%. Validated data quality using PyTest and Python.

Orchestrated the processing of 1TB+ datasets in Azure Databricks, achieving sub-10-minute job completion and eliminating data silos across departments.

Automated ETL workflows with Azure Data Factory and Azure Logic Apps, reducing manual intervention by 35%.

Built predictive models using Azure Machine Learning and Scikit-learn, achieving over 90% accuracy in demand forecasting, saving $50K+ annually.

Optimized replenishment strategies with Snowflake SQL, reducing order lead times by 25%. Integrated historical data from Azure Blob Storage/CSV to derive actionable patterns. Ensured GDPR compliance with Azure Key Vault and encryption mechanisms.

Developed Power BI dashboards to visualize inventory KPIs such as turnover rates and reorder points. Processed data using Python (Pandas, Matplotlib) and implemented DAX calculations, improving dashboard interactivity by 25%. Enabled row-level security for controlled access.

Automated updates to Azure DevOps boards using Python scripts, improving team productivity by 20%. Provided onboarding documentation for Event Hub streams and Data Factory pipelines.

Monitored workflows with Azure Monitor and Log Analytics, ensuring 99.9% uptime. Enforced data protection using Snowflake RBAC and Azure Key Vault, ensuring full GDPR compliance.

Integrated IoT data streams from Azure IoT Hub with structured warehouse data in Snowflake, providing a unified, real-time view of inventory and reducing discrepancies by 30%. Applied custom imputation logic in Python, boosting forecast accuracy by 5%.

Reduced cloud infrastructure costs by 18% using Azure Cost Management and Snowflake’s auto-suspend/resume features. Enabled zero-copy cloning for multi-team analytics, saving 25% on compute spend.

Scaled Azure Event Hub pipelines to process 1M+ updates daily across 50+ warehouses. Containerized pipelines using Docker and deployed them via Azure Kubernetes Service (AKS), increasing scalability and resilience.

Tech Talents, India

09/2019 – 02/2021

Data Engineer

Engineered ETL workflows using AWS Glue, S3, and PySpark for large-scale batch processing.

Designed data lake layers (raw, curated, trusted) with S3 zoning to support regulatory and analytical use cases.

Orchestrated workflows with Step Functions and Lambda for event-driven processing.

Developed optimized Redshift schemas using appropriate sort and distribution keys to minimize scan times.

Automated CDC using AWS DMS, ensuring reliable incremental loads from RDS and DynamoDB sources.

Enforced data security with KMS, IAM policies, and granular access control in S3.

Developed and executed unit tests (PyTest) and implemented Glue bookmarks to avoid reprocessing.

Enabled operational monitoring with CloudWatch, custom metrics, and automated alerts.

SKILLS

Cloud Platforms: Azure (Data Factory, Data Lake, Synapse, Databricks), AWS (S3, Glue, Redshift, Lambda, EMR & EC2)

Programming & Tools: Python, PL/SQL, PySpark, DBT, Git, Jenkins, Azure DevOps, Informatica, SSIS

Big Data & ETL: Spark, Hive, Delta Lake, Data Modeling, CI/CD, Data Pipelines

Databases: SQL Server, Snowflake, PostgreSQL, Redshift, Oracle

CRM/ERP: Salesforce CPQ

Others: Agile, PowerShell, Azure Key Vault, ARM Templates, Excel, Statistics,Airflow

EDUCATION

Master of Science: Data Science Wichita State University Aug 2022 – May 2024

Bachelor of Computer Science Aurora’s Degree & College, India Jul 2017 – Oct 2020

CERTIFICATION

Power BI Certification – Code Basics

SQL Certification – Code Basics

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