TEJASWEE LEKKALA

• Designed and deployed scalable Azure Data Factory (ADF) pipelines for ingesting and transforming large-scale structured and unstructured data from heterogeneous sources.
• Engineered batch and real-time data processing pipelines using DataBricks, PySpark, and Azure Stream Analytics, supporting enterprise-level OLAP and OLT systems.
• Developed SQL-based data integration and ETL logic for transforming and loading data into Snowflake and other cloud-based data warehouses.
• Implemented business logic through complex data transformations, developing custom Python UDFs for Spark SQL processing and analytics.
• Automated ingestion and transformation workflows using ADF triggers, including Schedule, Event, and Tumbling Window, enhancing pipeline reliability and maintainability.
• Built and maintained OLAP cubes for reporting and analysis, and supported OLT systems with real-time ingestion pipelines using Azure Event Hub and Service Bus Queues.
• Migrated legacy systems and on-prem data from Oracle and Teradata to Azure Data Lake Store (ADLS) using ADF V2.
• Developed and maintained CI/CD pipelines using Azure DevOps for deploying ADF components, DataBricks notebooks, and Snowflake procedures.
• Supported enterprise data consumption by integrating outputs into NoSQL (MongoDB) and search/visualization tools like Elasticsearch and Kibana.

Environment: Azure Data Factory, DataBricks, Snowflake, SQL, Python, PySpark, Azure Logic Apps, Azure Event Hub, Azure Stream Analytics, Azure DevOps, Oracle, Teradata, MongoDB, Elasticsearch, Kibana, OLAP, OLT.

• Ensured data integrity, quality, and security using best practices, including encryption, IAM roles, and monitoring with AWS CloudWatch.e, EMR, and Athena.
• Built and maintained ETL/ELT pipelines with AWS Glue, AWS Data Pipeline, and Python/PySpark, enabling seamless ingestion, transformation, and movement of large-scale structured and semi-structured datasets.
• Developed data warehousing solutions in Redshift, optimizing storage, query performance, and cost for analytics and reporting workloads.
• Implemented real-time streaming pipelines using Amazon Kinesis and Lambda, enabling near real-time analytics for transactional, IoT, and log data.
• Ensured data integrity, quality, and security using best practices, including encryption, IAM roles, and monitoring with AWS Cloud Watch.
• Automated deployment, scheduling, and monitoring of pipelines using CI/CD practices with Jenkins and Python scripts, improving reliability and reducing manual intervention.
• Developed interactive dashboards and reports using Power BI/Tableau integrated with AWS data services to support business intelligence and decision-making.

Environment: AWS (Redshift, S3, Glue, EMR, Lambda, Kinesis, Athena, Cloud Watch, EC2, VPC), Python, PySpark, SQL, Hive, Spark, ETL/ELT Frameworks, Power BI, Tableau, Jenkins, Unix/Linux, Data Warehousing, Data Governance & Security.

• Designed and implemented modern data architectures on Azure, leveraging Azure Data Lake Gen2, Azure Synapse Analytics, and Azure SQL Database for enterprise-scale analytics.
• Built and optimized ETL/ELT pipelines using Azure Data Factory (ADF) and Azure Data Bricks, supporting batch and near real-time data processing.
• Developed PySpark, Spark SQL, and Python-based data workflows, performing complex transformations, aggregations, and data cleansing for structured and unstructured datasets.
• Migrated legacy on-premises SQL Server, Oracle, and Hadoop workloads to Azure, enabling scalable, cost-efficient cloud solutions.
• Implemented real-time streaming pipelines with Azure Event Hub and Azure Stream Analytics for transactional and IoT data ingestion.
• Designed and deployed interactive Power BI dashboards and reports, integrating Synapse, Data Lake, and Data Bricks for business intelligence and predictive analytics.
• Automated pipeline scheduling, monitoring, and alerting using ADF triggers, Data Bricks jobs, and Azure Monitor, improving reliability and reducing manual intervention.
• Optimized performance, storage, and costs by leveraging modern features like serverless SQL pools, Delta Lake, and partitioning strategies in Synapse and Databricks.

Environment: Azure Data Lake Gen2, Azure Data Factory (ADF), Azure Data bricks, Azure Synapse Analytics, Azure SQL Database, Azure Event Hub, Azure Stream Analytics, PySpark, Spark SQL, Python, Power BI, SQL Server, Delta Lake.