Lokesh Bachu

• Designed and implemented a data engineering pipeline for medical data, leveraging Azure HDInsight (based on Apache Hadoop), Azure Data Lake Storage, and distributed computing systems.
• Led the ingestion process by utilizing Azure Data Factory, Apache Flume, and Apache Nifi to extract and ingest medical data from various sources, including electronic health records and medical devices.
• Developed and implemented data cleaning and transformation algorithms using the MapReduce framework in Azure HDInsight, ensuring data quality and integrity.
• Overcame scalability challenges by efficiently processing and analyzing large volumes of medical data in Azure, ensuring the pipeline's ability to handle increasing data volumes and processing requirements.
• Implemented data anonymization techniques using Azure Data Lake Analytics and Azure Databricks to ensure compliance with privacy regulations and protect patient confidentiality.
• Leveraged distributed databases like Azure Cosmos DB (with Cassandra API) and Apache HBase in Azure HDInsight for storing cleaned and transformed medical data, ensuring fault tolerance and high availability.
• Utilized Azure Databricks and Azure Synapse Analytics (formerly SQL Data Warehouse) for data analysis, enabling complex computations and machine learning tasks on the distributed data.
• Collaborated with cross-functional teams, including data scientists and healthcare professionals, to understand data requirements and design effective data models in Azure SQL Database and Azure Synapse Analytics.
• Implemented agile methodologies, enabling iterative development, frequent feedback loops, and adaptation to changing requirements in Azure DevOps.
• Ensured data governance by implementing best practices for data quality, security, and compliance with regulatory standards in Azure Data Catalog and Azure Purview.
• Implemented DevOps practices for continuous integration, automated testing, and deployment using Azure DevOps pipelines, ensuring smooth development and operation of the data engineering pipeline.
• Employed effective data modeling techniques to design schemas for storing medical data and optimize query performance in Azure SQL Database and Azure Synapse Analytics.

• Data Ingestion:
• Extracted data from various sources, including databases, APIs, and files.
• Utilized tools such as Apache Flume and Apache Nifi for efficient data ingestion.
• Implemented data validation and transformation processes during ingestion.
• Data Transformation and ETL (Extract, Transform, Load):
• Designed and implemented robust ETL processes to transform raw data into structured and consistent formats.
• Applied data cleansing techniques to ensure data quality and integrity.
• Used SQL and scripting languages (e.g., Python, Shell) for data transformation tasks.
• Data Modeling:
• Developed data models that captured the relationships and structure of the data.
• Designed star schemas and dimensional models for efficient data warehousing.
• Implemented optimization techniques to enhance query performance.
• Data Warehousing:
• Implemented end-to-end data warehousing solutions to support reporting and analytics.
• Created and maintained data warehouse architectures, including staging, integration, and presentation layers.
• Utilized technologies such as Apache Hive and Apache Impala for querying and analysis.
• Database Management:
• Administered and optimized databases used in data warehousing.
• Performed database tuning, indexing, and partitioning for improved performance.
• Implemented security measures and access controls to ensure data integrity and confidentiality.
• Data Integration:
• Integrated data from diverse sources, both internal and external, ensuring consistency and reliability.
• Implemented data integration pipelines using tools like Apache Kafka and Apache NiFi.
• Developed data integration workflows and data mapping strategies.
• Data Quality Management:
• Implemented data quality processes, including data profiling, cleansing, and validation.
• Monitored and resolved data quality issues to ensure accurate and reliable data.
• Developed and maintained data quality metrics and reporting mechanisms.
• Data Governance:
• Established data governance frameworks and policies for proper data management and usage.
• Ensured compliance with data governance standards and regulatory requirements.
• Implemented data governance processes for data lineage, metadata management, and data cataloging.
• Troubleshooting and Issue Resolution:
• Identified and resolved data-related issues, performing root cause analysis.
• Implemented preventive measures and conducted performance tuning to optimize system efficiency.
• Collaborated with cross-functional teams to address data-related challenges and provide technical expertise.
• Collaboration and Communication:
• Worked closely with data analysts, data scientists, and business stakeholders to understand requirements.
• Communicated effectively to translate business needs into technical data engineering solutions.
• Collaborated in cross-functional teams to deliver high-quality data-driven solutions.
• Emerging Technologies and Industry Trends:
• Kept up-to-date with the latest advancements in data engineering, including big data technologies, cloud computing, and automation.
• Evaluated and implemented emerging technologies to improve data engineering processes and efficiency.

Skills Utilized:

• Big Data Technologies (Apache Hadoop, HDFS, MapReduce)
• Apache Flume and Apache Nifi for Data Ingestion
• SQL and Scripting Languages (Python, Shell)
• Data Modeling (Star Schemas, Dimensional Models)
• Apache Hive and Apache Impala for Querying and Analysis
• Database Administration and Optimization
• Data Integration Tools (Apache Kafka, Apache NiFi)
• Data Quality Management
• Data Governance
• Troubleshooting and Performance Tuning
• Collaboration and Communication
• Emerging Technologies and Industry Trends Awareness
• Collaborated on ETL (Extract, Transform, Load) tasks, maintaining data integrity and verifying pipeline stability

• Test Planning: Collaborated with the development team to design comprehensive test plans and strategies.
• Test Case Development: Created detailed test cases and test scenarios based on project requirements.
• Test Execution: Conducted manual and automated testing to verify software functionality, performance, and usability.
• Defect Identification and Tracking: Identified and reported software defects using bug tracking systems, providing clear and concise bug reports.
• Test Automation: Developed and maintained automated test scripts using [automation tools/frameworks] to improve testing efficiency.
• Regression Testing: Performed regular regression testing to ensure software stability and maintain the integrity of existing features.
• Continuous Integration and Delivery: Integrated testing activities into the continuous integration and delivery pipeline to support agile software development practices.
• Test Data Management: Managed and maintained test data sets and test environments to ensure accurate and reliable testing.
• Performance Testing: Conducted performance testing to evaluate software responsiveness and scalability under various workloads.
• Documentation: Created comprehensive test documentation, including test procedures, test results, and other relevant information.
• Collaboration: Collaborated closely with cross-functional teams, including developers, business analysts, and project managers, to ensure effective communication and alignment of testing activities.
• Quality Assurance Processes: Contributed to the improvement and implementation of quality assurance processes and best practices.
• Continuous Learning: Stayed updated with emerging testing tools, techniques, and industry trends to enhance the quality assurance process and drive continuous improvement.