• Accomplished Data Engineer with over 4 years of experience designing, developing, and implementing robust data solutions.• Proficient in utilizing ETL tools such as Apache Spark, Talend, and Informatica to extract, transform, and load large volumes of data from diverse sources into data lakes or warehouses, facilitating accurate analytics and reporting.• Demonstrated expertise in database management systems including SQL Server, MySQL, and PostgreSQL, proficient in writing complex queries, optimizing database performance, and ensuring data integrity and security.• Skilled in programming languages such as Python, Java, and Scala for data manipulation, analysis, and automation tasks, leveraging frameworks like Pandas, NumPy, and Apache Hadoop to handle big data challenges effectively.• Worked on Data Warehouse design, implementation, and support (SQL Server, Azure SQL DB, Azure SQL Data warehouse, Teradata).• Experienced in cloud platforms such as AWS, Azure, and Google Cloud Platform, adept at deploying and managing data infrastructure services like Amazon Redshift, Azure Data Lake, and Google Big Query for scalable and cost-effective data solutions.• Implemented real-time data processing solutions using technologies like Apache Kafka, Apache Flink, and AWS Kinesis, enabling timely insights and actions based on streaming data sources.• Proficient in data modelling techniques including relational, dimensional, and NoSQL data modelling, with hands-on experience in designing schemas for efficient data storage, retrieval, and analysis.• Expertise in version control systems such as Git, ensuring collaborative development and tracking changes in data engineering projects, with a strong focus on code quality and documentation standards.• Collaborated closely with cross-functional teams including data scientists, analysts, and business stakeholders to understand requirements, define data solutions, and deliver actionable insights for business growth and innovation.• Proficient in leveraging Kubernetes to orchestrate and manage containerized applications, ensuring scalability, reliability, and efficiency in data processing pipelines.• Implemented automated testing and monitoring processes using tools like Apache Airflow, Jenkins, and Grafana to ensure data quality, pipeline reliability, and performance optimization.