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2 Days ago

Senior Data Engineer

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Information Technology
Full-Time
Quest Global

Overview

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

  • 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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