Quest Global
From Quest Global's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
Job Requirements
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
Years of Experience: 4-6 Years
Strong hands-on expertise in Spark, Airflow, and SQL.
Deep understanding and experience with modern Data Platforms – Cloudera Data Platform (CDP)
Strong hands-on experience with PySpark.
Experience with Cloudera Data Platform (CDE, CDW, Ozone, Airflow, SDX), Apache Ranger
Deep understanding of distributed data systems and Hive Metastore
Experience and understanding of cataloging, lineage, and governance
Experience / understanding Open Data Contract Standard (ODCS) and its implementation
Experience working with SQL, file formats (Iceberg/Parquet), and partitioning/bucketing strategies.
Nice to have
Experience modernizing enterprise finance systems or regulated environments
Knowledge of CI/CD, data engineering best practices
Understanding of financial data structures, accounting processes, or reconciliation workflows.
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