Support the design, implementation, and maintenance of enterprise ETL processes for data platforms, for a global client base.
Develop scalable and efficient code to process data, ensuring availability and accessibility in a timely manner.
Leverage big data processing frameworks such as Apache Spark and Hadoop to build and optimize data pipelines.
Collaborate with senior engineers to address data challenges, contributing to solutions that maintain high data quality.
Assist in the data delivery process, working alongside Data Engineers and Analysts to support accurate, high-value data solutions across various clients and industries.
Build strong working relationships with team members and clients, contributing to both local and global projects.
Learn and apply industry best practices, including version control, code reviews, and data validation, to ensure quality in data processes.
Use SQL and other database technologies to help optimize data processing and reduce the time required to handle large data sets.
Design, implement, and maintain data pipelines using ETL frameworks, orchestration tools, and distributed data processing engines.
Participate in efforts to automate routine data tasks and streamline processes.
Work Experience
Keyskills: cloudera hive continuous integration data pyspark ci/cd data pipeline apache ranger sql apache data modeling spark data structures hadoop etl big data etl scripts cd workflow airflow data processing engineering data engineering reconciliation data quality system etl process
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