Senior Data Engineer, Dhahran - Saudi Arabia (6 month initial contract)
In order to be eligible for this contract opportunity you will need to be based in SAUDI ARABIA and have a TRANSFERABLE IQAMA.
Responsibilities:Understanding of machine learning model requirements in terms of data preparation, feature engineering, and data augmentation. Skills in implementing data pipelines and workflows using tools like Apache Airflow, Luigi, or similar technologies. Understanding of big data technologies and practices, including data lakes, data streams, and real-time data processing. Demonstrated ability to design and implement effective data architecture that supports large-scale, high-performance data processing. Ability to work closely with data scientists, machine learning engineers, and software developers to understand data needs and deliver solutions that support AI model development. Eagerness to explore new tools and technologies that can enhance the data infrastructure and support the evolving needs of generative AI product development. Experience with version control systems (e.g., Git) and collaboration tools (e.g., Git Lab) for managing codebases and documentation.
Skills / Experience Required:Bachelor degree in computer science, Data Science, Information Technology, or a related field with a strong emphasis on data engineering, database design, or big data technologies. Proven experience in data engineering, preferably in AI/ML or Gen AI environments. Proficiency in Python is a must and other programming languages relevant to data engineering, such as Scala, or Java, and familiarity with Big Data processing frameworks (e.g., Apache Spark, Hadoop). Build and maintain data lakes, warehouses, and feature stores optimized for Gen AI workloads. Experience with relational SQL and NoSQL databases, including MySQL, Postgre SQL, Mongo DB, and Cassandra. Knowledge of data modelling, ETL (Extract, Transform, Load) processes, and data warehousing solutions. Strong technical background in AI technologies, including machine learning, natural language processing, and deep learning. Knowledge of MLOps tools and practices (e.g., MLflow, DVC).
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