Data Engineer Information Systems

BHUVAN SREE SAI REDDI

630-***-**** ******************@*****.*** LinkedIn Chicago, IL

Professional Summary

Data Engineer with 4+ years of experience in AWS, Azure, and big data technologies, focused on delivering high-performance ETL pipelines, real-time streaming, and modern data warehouses

Educational Details

• Master of Science: Information Systems from Depaul University – Chicago, IL, USA Completed course work: Database Design for Information Systems, Python Programming, Enterprise Cloud Computing, Statistics and Data Analysis, Data Visualization, System Analysis & Design, Database Programming, Fundamentals of Data Science, BI and Analytics Systems

• Bachelor of Technology: Mechanical engineering from RVR&JC College of Engineering – Guntur, India Completed course work: C programming laboratory, Problem Solving with C, Differential Equations and Statistics, Calculus and Numerical Methods, Computer Applications, Data Structures and Algorithms. Technical Skills

• Programming & Frameworks: Python (PySpark, Pandas, NumPy), SQL, Spark SQL, Bash

• Big Data & Streaming: Apache Spark, Apache Kafka, Hadoop, Databricks

• Cloud Platforms: AWS (EMR, S3, Lambda, IAM), Azure (Databricks, Data Factory, ADLS Gen2, Synapse, DevOps)

• ETL & Orchestration: Azure Data Factory (ADF), SSIS, Apache Airflow, Informatica (familiar), Talend (familiar)

• Data Modeling & Warehousing: Dimensional Modeling (Star & Snowflake Schema), ER Modeling, Snowflake, PostgreSQL, Teradata, SQL Server, Redshift

• Data Governance & Quality: Data Validation, Schema Enforcement, Lineage Tracking, Observability, Collibra, Alation

• BI & Visualization: Tableau, Power BI, Excel

• Infrastructure & DevOps: Docker, Terraform, Jenkins, GitHub, Azure DevOps, Linux/Unix

• Methodologies: Agile, Scrum, SDLC, Unit Testing (PyTest) Work Experience

Data Engineer Discover Financial Services Riverwoods, IL Jan2024 – Present Project: Fraud Detection & Real-Time Analytics

• Improved fraud detection speed from minutes to seconds by developing real-time ETL pipelines in Databricks (PySpark, Kafka) that process tens of millions of card transactions daily. Optimized Spark jobs on AWS EMR with partitioning and autoscaling, lowering compute usage by 15% while meeting SLA requirements.

• Enhanced pipeline accuracy by 25% by embedding Python-based data validation and governance checks into Airflow DAGs for schema integrity, row counts, and null thresholds before loading to Snowflake.

• Accelerated ML model retraining cycles by designing curated dimensional feature datasets (star schema) in Snowflake and PostgreSQL, reducing turnaround from weeks to days. Delivered executive reporting dashboards in Tableau with near real-time refresh, providing visibility into fraud KPIs such as chargebacks and declines.

Data Engineer Infosys, India Nov 2021 – Jan 2023 Project: Data Lake Modernization (Azure)

• Reduced daily ETL runtime by 30% by migrating terabytes of financial data from Oracle into Azure Databricks (PySpark) pipelines orchestrated with ADF and Airflow. Automated 40+ ingestion workflows using ADF and Airflow, ensuring reliability and SLA compliance.

• Increased BI adoption by 40% by building PostgreSQL marts using dimensional modeling (star schema) to support self-service Power BI dashboards. Enhanced customer data accuracy by 25% with Python/SQL deduplication and cleansing of millions of records.

• Delivered infrastructure automation with Terraform and Azure DevOps, reducing environment provisioning time from days to hours. Project: Financial Data Migration (Teradata Snowflake)

• Achieved 99.9% data reconciliation accuracy by migrating billions of rows across 150+ financial tables into Snowflake with audit checks and lineage validation. Improved query performance by ~40% through schema redesign and clustering strategies in Snowflake.

• Reduced maintenance overhead by decommissioning 200+ Teradata objects after dependency analysis. Delivered zero-downtime migration with staged incremental loads, checksums, and automated error handling in Python/Snowflake Tasks.

• Standardized data transformations by converting legacy BTEQ/macros into parameterized SQL stored procedures and reusable UDFs. Data Engineer Citius Tech, India Sep 2020 – Oct 2021 Project: Healthcare Data Integration & Reporting

• Automated ingestion of hundreds of HL7 feeds and flat files into Hadoop with PySpark, reducing manual integration effort by 40%.

• Improved clinical reporting accuracy by 30% by standardizing ICD-10 codes and lab results for 5M+ patient records using SQL and Python. Accelerated data refresh cycles by migrating pipelines to Azure Data Factory (ADF) and Synapse with partitioned and parallel loads.

• Developed Power BI dashboards to track readmissions, patient flow, and quality metrics, enabling quicker clinical decisions.

• Ensured HIPAA compliance by configuring RBAC, encryption policies, and key rotation in Azure ADLS/Blob, reinforcing data governance and security standards.

Data Analyst Intern Citius Tech India May 2020 – Aug 2020 Project: Clinical Data Reporting Automation

• Improved monthly reporting efficiency by 35% by transforming millions of clinical records with SQL Server and SSIS ETL pipelines. Developed Power BI dashboards covering patient outcomes and departmental KPIs, reducing reliance on ad-hoc requests.

• Increased consistency across healthcare datasets by normalizing schemas and standardizing formats.

• Strengthened reliability of refresh cycles by embedding validation checks for row counts, null thresholds, and referential integrity in SSIS, ensuring data quality compliance. Automated recurring extracts and parameterized SQL jobs in Python, freeing analyst time for higher-value work.

Certifications

• Databricks Fundamentals Accreditation - Databricks (Link)

• SQL for Data Engineering – LinkedIn (Link)

• SQL (Intermediate) – HackerRank (Link)

• Microsoft Certified: Azure Data Fundamentals (DP-900)

• Microsoft Certified: Azure Fundamentals (AZ-900)

• AWS Certified Cloud Practitioner (CLF-C02)

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