Akash K Email and Phone Number

• Designed, developed, and maintained ETL processes using Scala in Azure Data Factory, ensuring seamless data movement and transformation across diverse sources.• Optimized Big Query data structures, resulting in a 25% decrease in query execution time and a 30% cut in storage expenses.• Used pandas, NumPy, Seaborn, SciPy, matplotlib, sci-kit-learn, and NLTK in Python to develop various machine learning algorithms.• Collaborated with data engineers and the operation team to implement the ETL process. Wrote and optimized SQL queries to perform data extraction to fit the analytical requirements. Data sources are extracted, transformed, and loaded to generate CSV data files with Python programming and SQL queries.• Utilized PySpark to perform complex data transformations, aggregations, and cleansing operations to ensure data quality and consistency throughout the pipeline.• Developed and maintained ETL processes using PL/SQL scripts, optimizing queries and code for performance and implementing data validation routines.• Developed and maintained data pipelines using Kafka and KSQL, ingesting and processing large volumes of data in real-time.• Implemented Machine Learning, Computer Vision, Deep Learning, and Neural Networks algorithms using TensorFlow and designed Prediction Models using Data Mining Techniques with the help of Python, and Libraries like NumPy, SciPy, Matplotlib, Pandas, scikit-learn.• Integrated data from various sources using C# and Azure services, designing and maintaining pipelines with Python libraries for data quality checks and validation processes.• Worked on creating dashboards with Tableau to explore larger datasets for business users and created visualizations.

• Utilized Azure Data Factory and Azure Data Lake Gen2 for loading data into the data platform.• Developed, maintained, and optimized ETL processes using SQL Server Integration Services (SSIS).• Used SQL Server and Python (including Pandas and NumPy libraries) for data cleansing, transformation, and enrichment.• Administered SQL Server databases and Azure SQL Databases, ensuring optimal performance, scalability, and security.• Designed and implemented data models and schemas in SQL Server and Azure SQL Database for optimized data storage and retrieval.• Designed and maintained ETL (Extract, Transform, and Load) processes to move data between SAP systems and big data platforms.• Integrated MDM solutions with BI platforms like MicroStrategy to provide unified data views and support end-user self-service analytics.• Implement Azure services like Azure Data Factory, Azure Data Bricks, Azure Data Lake Gen2, and Azure Blob Storage for data storage, processing, and analysis.• Managed and prioritized data engineering projects, ensuring alignment with business goals and timelines.

• Integrated ETL pipelines and APIs with existing healthcare systems and data sources to streamline data flow and support healthcare analytics.• Supported data migration and upgrade projects for SAP HANA and big data platforms, ensuring smooth transitions and continuity of data services.• Developed, refined, and enhanced ETL processes with SQL, Python, and Snowflake, ensuring efficient data flow.• Designed and deployed data models and schemas within Snowflake, ensuring structured and efficient storage.• Administered Snowflake databases on AWS, optimizing performance, scalability, and security for seamless operations.• Implemented robust data quality checks, validation procedures, and profiling methods to maintain data integrity.• Collaborated with stakeholders to comprehend data needs, aligning projects with organizational objectives.• Employed agile methodologies to manage and prioritize data engineering initiatives for timely delivery.• Utilized AWS services like S3 and EC2 for efficient data storage and processing.• Developed and maintained Python scripts for streamlined data processing and ETL operations.• Maintained comprehensive documentation of data sources, ETL workflows, and data models for transparency and reproducibility.

• Involved in analyzing business requirements and prepared detailed specifications that follow project guidelines required for project development. Involved in Data Ingestion using Sqoop/Flume.• Used Kafka functionalities like distribution, partition, and replicated commit log service for messaging systems by maintaining feeds. Experience in using Kafka as a messaging system to implement real-time Streaming solutions using Spark Streaming. • Worked on Spark Data sources, Spark Data Frames, Spark SQL, and Streaming using Scala. Good knowledge of creating data frames using Spark SQL.• Migrated Hive QL queries structured into Spark SQL to improve performance. Developed PySpark/Spark SQL scripts to analyze various customer behaviors.• Extensively worked with PySpark/Spark SQL for data cleansing and generating Data Frames and RDDs. • Developed Spark code using Python for PySpark, Scala, and Spark-SQL for faster testing and processing of data. • Explored with Spark improving the performance and optimization of the existing algorithms in Hadoop using Spark context, Spark-SQL, Spark Streaming, Data Frame, pair RDD’s, and Spark YARN.

• Designed and implemented efficient SQL database schemas for optimized data organization. And optimized database structures and indexing strategies to enhance performance and scalability.• Developed and maintained ETL processes for seamless data extraction from diverse sources.• Loaded and managed data in Google Cloud Storage and BigQuery, optimizing processes for speed and reliability.• Crafted SQL queries for data analysis and reporting in Google BigQuery, extracting meaningful insights.• Orchestrated data warehouses in BigQuery, implementing partitioning and clustering strategies for optimal query performance.• Utilized tools like Apache Airflow and Google Cloud Composer for orchestrating ETL workflows.• Utilized Git for version control, managing code versions, and tracking changes in collaborative environments.• Collaborated with team members and stakeholders on data projects to ensure alignment with organizational objectives.• Implemented CI/CD practices for data pipelines, automating testing and validation for reliable deployments.• Documented data engineering processes, code, and configurations for comprehensive understanding and knowledge transfer.