Job Description Below:- Job Title: Cloud Data Engineer About the Role • The Cloud Data Engineer will be responsible for developing, expanding, and optimizing our data pipeline architecture, as well as optimizing data flow and collection for cross-functional teams. • The ideal candidate is an experienced data pipeline builder and data wrangler who enjoys optimizing data systems and building them from the ground up. • During various aspects of this process, you should collaborate with coworkers to ensure that your approach meets the needs of each project. • To ensure success as a data engineer, you should demonstrate flexibility, creativity, and the capacity to receive and utilize constructive criticism. • A formidable data engineer will demonstrate insatiable curiosity and outstanding interpersonal skills. Qualifications • Bachelor's Degree in Computer Science or a related technical field, and 5+ years of relevant employment experience. • ETL Developer • Informatica Power Center and Informatica Cloud (IDMC) experience required • SQL Server experience required • Experience operating very large data warehouses or data lakes. • 2+ years of work experience with ETL and Data Modeling on AWS Cloud Databases. • 3+ years SQL Server. • 3+ years of Informatica or similar technology. • Snowflake experience is a bonus • Experience migrating ETL from on-prem to Cloud platforms is a bonus • Knowledge of the Financial Services industry. Preferred Qualifications • 5+ years of work experience with ETL and Data Modeling on AWS Cloud Databases. • Experience migrating on-premise data processing to AWS Cloud. • Relevant AWS certification (AWS Certified Data Analytics, AWS Certified Database, etc.). • Expertise in ETL optimization, designing, coding, and tuning big data processes using Informatica Data Management Cloud or similar technologies. • Experience with building data pipelines and applications to stream and process datasets at low latencies. • Show efficiency in handling data - tracking data lineage, ensuring data quality, and improving the discoverability of data. • Sound knowledge of data management and knows how to optimize the distribution, partitioning, and MPP of high-level data structures. • Knowledge of Engineering and Operational Excellence using standard methodologies.