Bhargav Raju

Data Engineer with 4+ years of experience designing, developing, and supporting scalable data pipelines and cloud-based data platforms. Hands-on experience with Python, SQL, PySpark, Apache Spark, Azure Data Factory, Apache Airflow, Snowflake, PostgreSQL, AWS, Azure, and GCP. Experienced in building and maintaining ETL/ELT pipelines for data ingestion, transformation, validation, and analytics.

Worked with structured and semi-structured data formats including CSV, JSON, and Parquet. Strong experience in data quality validation, SQL optimization, workflow automation, production monitoring, and troubleshooting. Worked with cloud storage and data services such as AWS S3, Azure Data Factory, Snowflake, PostgreSQL, and BigQuery.

Hands-on experience with Docker, Kubernetes, Git, Jenkins, and CI/CD pipelines for deployment and operational support. Experienced in supporting batch-processing workflows, scheduled data pipelines, and downstream analytics datasets. Familiar with REST API integrations, Agile/Scrum development, production support, and cross -functional collaboration with engineering and business teams.

Data Engineer - Uniphore Inc.

(2023-06)

• Developed and supported cloud-based data ingestion and processing workflows using Python, SQL, PostgreSQL, Snowflake, and AWS, supporting structured datasets across multiple source systems.
• Automated recurring data ingestion, validation, and operational workflows using Python and SQL, reducing manual data-processing effort by approximately 25–30%.
• Worked with AWS S3 and PostgreSQL data sources to ingest CSV and relational datasets into downstream data platforms for analytics and application use.
• Supported data connector integrations for relational databases and object -storage sources, validating schema discovery, metadata ingestion, column information, and downstream data availability .
• Developed and maintained SQL queries for data verification, reconciliation, transformation, and troubleshooting across PostgreSQL and Snowflake environments.
• Processed structured and semi-structured datasets including CSV and JSON, validating schemas, keys, relationships, and column-level metadata before downstream ingestion.
• Standardized schema and metadata validation processes across multiple datasets, helping reduce ingestion -related data-quality issues by approximately 15%.
• Automated recurring workflow execution and operational checks using Python and Bash, improving processing consistency and reducing repetitive support activities.
• Created database schemas, tables, primary keys, and foreign-key relationships for reference datasets used in downstream data discovery and analytics workflows.
• Loaded and validated relational datasets into Snowflake and PostgreSQL, verifying table structures, relationships, row-level data, and overall data completeness.
• Generated and validated table relationship metadata and supported development of relationship diagrams for complex multi-table datasets.
• Investigated data-ingestion and production issues using SQL queries, application logs, REST API responses, and platform logs, reducing average troubleshooting time by approximately 20%.
• Supported Docker and Kubernetes-based environments, reviewing pod logs, application health, configurations, and service failures during production troubleshooting.
• Built and maintained monitoring and operational visibility using Datadog, Prometheus, and ELK, helping engineering teams identify infrastructure and application issues earlier.
• Supported CI/CD-based deployments and validation activities, reducing manual release effort by approximately 20% while improving consistency across environments.
• Collaborated with data engineers, application developers, platform engineers, and product teams in an Agile/Scrum environment to deliver data integration and platform enhancements.
• Environment: Python, SQL, PostgreSQL, Snowflake, AWS S3, EC2, REST APIs, CSV, JSON, Docker, Kubernetes, Git, CI/CD, Datadog, Prometheus, ELK, Jira

Data Engineer - Truist Bank

(2022-08 - 2023-06)

• Developed responsive web application features using React.js, JavaScript, JSX, HTML5, and CSS3 for customer-facing and internal banking application modules.
• Developed and supported 10+ ETL/ELT pipelines using Python, SQL, and Azure Data Factory, automating data ingestion and processing across multiple source systems.
• Built data transformation workflows using PySpark and SQL for data cleansing, validation, aggregation, and preparation of analytics-ready datasets.
• Implemented Python and PySpark automation for scheduled data-processing workflows, reducing manual data operations by approximately 30%.
• Developed and maintained Azure Data Factory pipelines for ingestion, transformation, workflow dependencies, and scheduled batch processing.
• Created SQL-based data-quality checks to identify missing, duplicate, and inconsistent records, reducing downstream data-quality issues by approximately 20%.
• Processed structured and semi-structured data in CSV, JSON, and Parquet formats, applying validation and transformation logic before downstream loading.
• Optimized SQL queries and PySpark transformations for larger datasets, improving processing efficiency by approximately 20–25%.
• Monitored production data workflows and investigated failed jobs, performance issues, and application logs, helping reduce recurring pipeline failures by approximately 20%.
• Automated workflow scheduling, health checks, and failure notifications using Python and Apache Airflow, reducing troubleshooting time by approximately 25%.
• Worked with relational databases and cloud data platforms to retrieve, transform, validate, and load business data for downstream reporting and analytics.
• Supported pipeline deployments across development, QA, and production environments, validating dependencies, configurations, and post-deployment data quality.
• Used Git and GitHub for source control, feature branching, pull requests, code reviews, and collaboration across engineering teams.
• Participated in Agile ceremonies including sprint planning, daily stand-ups, defect resolution, testing, and production release support.
• Collaborated with developers, analysts, QA engineers, and business teams to troubleshoot data-quality issues and document recurring pipeline problems and resolutions.
• Environment: Python, SQL, PySpark, Apache Spark, Azure Data Factory, Apache Airflow, Snowflake, CSV, JSON, Parquet, Git, GitHub, Jenkins, Jira, Linux

Java Developer Intern - Maxwell Information Systems

(2020-04 - 2021-07)

• Assisted senior developers with backend and data-related application functionality using Java, SQL, and REST APIs.
• Created and modified 10+ SQL queries for data retrieval, insertion, updates, validation, and testing activities.
• Worked with relational databases to support application data extraction, validation, and troubleshooting.
• Assisted with REST API integrations and JSON request/response processing for application workflows.
• Used Postman to test REST endpoints, validate responses, and troubleshoot application and data-related issues.
• Supported data validation and defect-resolution activities, helping improve development and testing efficiency by approximately 10–15%.
• Used Git for version control, including branching, commits, merges, and collaboration with the development team.
• Participated in application debugging, functional testing, defect fixes, and technical documentation.
• Worked in an Agile development environment, participating in sprint assignments, daily stand-ups, testing, and team discussions.
• Environment: Java, SQL, REST APIs, JSON, PostgreSQL/MySQL, Git, Postman, Jira, Agile

Master of Science - Computer Science - Campbellsville University, Campbellsville, KY (2021-08 - 2023-12)

Bachelor of Science - Computer Science - Gitam University (2017-06 - 2021-06)