Job Description Summary
Role:
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.
Comply with all Client internal policies and adhere to external regulations.
All About You:
Experience as a Data Engineer or in a similar role, with a strong understanding of data engineering concepts and methodologies.
Strong knowledge of writing and optimizing SQL queries to retrieve, manipulate, and analyze data efficiently.
Hands-on experience with big data technologies such as:
o Apache Spark (PySpark, Spark SQL, Spark Streaming)
o Hadoop ecosystem (HDFS/ Ozone, Hive, YARN)
Familiarity with ETL frameworks and the ability to design, implement, and maintain data pipelines.
Understanding data modeling concepts and database design to support scalable data solutions.
Familiarity with Python.
Ability to analyze and troubleshoot data issues and provide solutions with minimal supervision.
Basic knowledge of testing and validating data to ensure accuracy and consistency in data pipelines.
Excellent verbal and written communication skills, with the ability to articulate complex ideas clearly and concisely to both technical and non-technical stakeholders.
Bachelor's degree in quantitative discipline such as Engineering, Mathematics, Finance, Business, or a related field. Equivalent practical experience may also be considered.
