Data Engineer Analyst

Role Overview: As a Cloud Data Engineer, you will be responsible for designing and implementing data warehouses, data lakes, and data marts for large financial institutions. You will work with technology and business leads to build or enhance critical enterprise applications in the cloud (AWS) and on-premises. Your role will involve working with the Modern Data Stack containing Snowflake Data Platform and Starburst Data Virtualization tool for Semantic Layer Build out. You are expected to have in-depth knowledge of current and emerging technologies and demonstrate a passion for designing elegant solutions and continuous self-improvement. Key Responsibilities: - Manage data analysis and data integration of disparate systems. - Translate functional specifications into technical designs for implementation and deployment in collaboration with business users. - Extract, transform, and load large volumes of structured and unstructured data into AWS data lakes or data warehouses. - Develop prototypes, produce design artifacts, develop components, and support testing and bug fixing with cross-functional team members. - Optimize data pipelines jobs for performance and scalability. - Implement data quality and data validation processes to ensure data accuracy and integrity. - Provide problem-solving expertise and complex analysis of data for business intelligence integration designs. - Convert physical data integration models and other design specifications to source code. - Ensure high quality and optimum performance of data integration systems to meet business solutions. Qualifications Required: - Bachelor's Degree in Information Technology, Information Systems, Computer Science, Software Engineering, or a related field. Experience in the financial services or banking industry is preferred. - 5 years of experience as a Data Engineer, focusing on building data pipelines and processing large datasets using Informatica PowerCenter and IDMC. - 2-3 years of experience with Informatica Data Quality (IDQ). - 5 years of experience with data warehouses, proficient in writing and understanding complex SQL queries, PL/SQL scripts, and Unix scripts. - 2-3 years of experience with Snowflake Data Platform is highly desirable. Exposure to Data Virtualization Platforms (Starburst, Denodo) is a plus. - 1-2 years of proficiency in AWS services including Glue, Redshift, EMR, RDS, Kinesis, S3, Athena, DynamoDB, Step Functions, and Lambda. - 1-2 years of expertise in Hive QL, Python programming, experience with Spark, Python, Scala, and Spark for big data processing and analysis. - Solid understanding of data modeling, database design, and ETL principles. - Experience working with data lakes, data warehouses, and distributed computing systems. - Familiarity with data governance, data security, and compliance practices in cloud environments. - Strong problem-solving skills and the ability to optimize and fine-tune data pipelines and Spark jobs for performance. - Excellent communication and collaboration skills, with the ability to work effectively in a team environment. Role Overview: As a Cloud Data Engineer, you will be responsible for designing and implementing data warehouses, data lakes, and data marts for large financial institutions. You will work with technology and business leads to build or enhance critical enterprise applications in the cloud (AWS) and on-premises. Your role will involve working with the Modern Data Stack containing Snowflake Data Platform and Starburst Data Virtualization tool for Semantic Layer Build out. You are expected to have in-depth knowledge of current and emerging technologies and demonstrate a passion for designing elegant solutions and continuous self-improvement. Key Responsibilities: - Manage data analysis and data integration of disparate systems. - Translate functional specifications into technical designs for implementation and deployment in collaboration with business users. - Extract, transform, and load large volumes of structured and unstructured data into AWS data lakes or data warehouses. - Develop prototypes, produce design artifacts, develop components, and support testing and bug fixing with cross-functional team members. - Optimize data pipelines jobs for performance and scalability. - Implement data quality and data validation processes to ensure data accuracy and integrity. - Provide problem-solving expertise and complex analysis of data for business intelligence integration designs. - Convert physical data integration models and other design specifications to source code. - Ensure high quality and optimum performance of data integration systems to meet business solutions. Qualifications Required: - Bachelor's Degree in Information Technology, Information Systems, Computer Science, Software Engineering, or a related field. Experience in the financial services or banking industry is preferred. - 5 years of experience as a Data Engineer, focusing on building data pipelines and p