Spark Data Engineer

Candidate should have 6-7 years of experience in Glu, Redshift, Quicksite, AWS EMR for spark implementation, Spark. Experience with one or more ETL tools like Informatica PowerCenter Databases: A strong understanding of relational databases (such as MySQL, Oracle, and SQL Server) and knowledge of SQL is essential. Experience with NoSQL databases like MongoDB or Cassandra is a plus. Data Warehousing Solutions: Familiarity with data warehousing architectures Programming Languages: Excellent knowledge of scripting and programming languages for data manipulation and transformation. Big Data Technologies: Knowledge of big data technologies can be beneficial, especially for handling large-scale data processing. Analytical Skills Data Analysis: Ability to analyze data sources, understand data relationships, and develop strategies to extract, transform, and load data effectively. Problem-Solving: Skills in identifying issues within data processes and developing innovative solutions. Soft Skills Communication: Strong oral and written communication skills are necessary for collaborating with team members and stakeholders, documenting ETL processes, and explaining technical details clearly. Attention to Detail: Precision is crucial in ETL development to ensure the accuracy and integrity of data. Project Management: The ability to manage projects, including planning, execution, and monitoring tasks, is valuable, especially for leading ETL projects or teams.