V
Venkata Ogirala
Venkata Ogirala
About
Detail
Pontiac, Michigan, United States
Experienced Data Engineer with over 6+ years of expertise in designing and building scalable data solutions across industries like Finance, Retail and Healthcare. Proficient in Big Data technologies, including Spark, Scala, PySpark, Snowflake, and Cloud platforms (GCP, Azure), with a strong focus on data migration and integration. I am skilled in developing real-time data pipelines, optimizing data processing for risk assessment, and supporting business analytics with BI tools like Power BI and Tableau. Proven ability in migrating large datasets, automating ETL workflows, and implementing microservices architecture using .NET Core and Dapr. Led cross-functional teams, mentored junior engineers, and utilized CI/CD practices for version control and tracking.
Strong experience in Software Development Life Cycle (SDLC) including Requirements Analysis, Design Specification and Testing as per Cycle in both Waterfall and Agile methodologies.
Adept at working in multi-cloud environments, including AWS, Azure, and GCP, with proven success in designing cloud-native data solutions tailored to cost-efficiency, performance, and scalability.
Proficient in scripting languages such as Python, PySpark and Scala, enabling seamless integration of custom functionalities into data pipelines.
Built and maintained batch and real-time data pipelines using Apache Spark, PySpark, Kafka, Airflow, and DBT, handling diverse data types including structured, semi-structured, and streaming data.
Expertise in data warehousing and analytics platforms such as Snowflake, BigQuery, and Redshift, with extensive experience in building data models, performance tuning, and data governance.
Led cloud migration initiatives involving legacy systems, orchestrating data movement, schema transformation, and validation with zero data loss and minimal downtime.
Developed modular, production-grade ETL pipelines integrating data from sources such as APIs, IoT devices, flat files, and relational databases using tools like AWS Glue and Azure Data Factory.
Strong experience in building audit frameworks, setting up data quality validation layers, and writing reusable unit tests using Pytest to ensure pipeline robustness and SLA compliance.
Proficient in managing CI/CD pipelines and Infrastructure as Code (laC) using GitHub Actions, Terraform, and Azure DevOps, ensuring automated deployments and environment consistency.
Collaborated cross-functionally with business analysts, DevOps engineers, and product managers to translate data needs into scalable technical solutions that support BI and ML workloads.
Built and published interactive dashboards in Power BI and Tableau to visualize key metrics, operational health, and pipeline performance in real-time.
Mentored junior engineers, conducted internal workshops on DBT, Snowflake, and Spark, and actively contributed to peer reviews and documentation processes to raise team technical standards.
Continuously driven by a passion for data innovation, delivering clean, reliable, and governed datasets that empower decision-makers and improve business outcomes.
Strong experience in Software Development Life Cycle (SDLC) including Requirements Analysis, Design Specification and Testing as per Cycle in both Waterfall and Agile methodologies.
Adept at working in multi-cloud environments, including AWS, Azure, and GCP, with proven success in designing cloud-native data solutions tailored to cost-efficiency, performance, and scalability.
Proficient in scripting languages such as Python, PySpark and Scala, enabling seamless integration of custom functionalities into data pipelines.
Built and maintained batch and real-time data pipelines using Apache Spark, PySpark, Kafka, Airflow, and DBT, handling diverse data types including structured, semi-structured, and streaming data.
Expertise in data warehousing and analytics platforms such as Snowflake, BigQuery, and Redshift, with extensive experience in building data models, performance tuning, and data governance.
Led cloud migration initiatives involving legacy systems, orchestrating data movement, schema transformation, and validation with zero data loss and minimal downtime.
Developed modular, production-grade ETL pipelines integrating data from sources such as APIs, IoT devices, flat files, and relational databases using tools like AWS Glue and Azure Data Factory.
Strong experience in building audit frameworks, setting up data quality validation layers, and writing reusable unit tests using Pytest to ensure pipeline robustness and SLA compliance.
Proficient in managing CI/CD pipelines and Infrastructure as Code (laC) using GitHub Actions, Terraform, and Azure DevOps, ensuring automated deployments and environment consistency.
Collaborated cross-functionally with business analysts, DevOps engineers, and product managers to translate data needs into scalable technical solutions that support BI and ML workloads.
Built and published interactive dashboards in Power BI and Tableau to visualize key metrics, operational health, and pipeline performance in real-time.
Mentored junior engineers, conducted internal workshops on DBT, Snowflake, and Spark, and actively contributed to peer reviews and documentation processes to raise team technical standards.
Continuously driven by a passion for data innovation, delivering clean, reliable, and governed datasets that empower decision-makers and improve business outcomes.
