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About the Job
The company is an ADX-listed public company, majority-owned by Abu Dhabi based G42, and is recognized as the region’s leading big data analytics company powered by GenAI. We utilize big data, analytics, and AI expertise to create significant business and societal impacts across varied sectors. Our aim is to drive data interpretation that supports informed decision-making, fostering safer, healthier, and more sustainable societies. With our suite of GenAI-driven products, we assist both private and public sectors in realizing their AI ambitions swiftly.
As a Senior Data Engineer, you will build and maintain the infrastructure for data collection, processing, and storage. Collaborating closely with data scientists, analysts, and other stakeholders, you will ensure that our data systems are reliable, scalable, and secure. This role focuses on developing and optimizing the company’s data infrastructure by designing and implementing data pipelines, ensuring data quality, and partnering with cross-functional teams on various data initiatives.
Responsibilities:
- Develop and maintain data systems to support strategic goals, focusing on data pipeline development, data quality, and collaboration.
- Design, construct, and maintain scalable data pipelines aimed at machine learning and analytics.
- Collaborate with data scientists and stakeholders to guarantee data accessibility, consistency, and reliability.
- Develop and maintain APIs for data access, integrate external data services, and manage data storage solutions for structured and unstructured data.
- Implement processes to monitor data quality and ensure accuracy for key stakeholders.
- Ensure compliance with data security and privacy policies.
- Maintain clear documentation, including data dictionaries and architectural diagrams.
Qualifications:
- Bachelor's degree in Computer Science, Engineering, Mathematics, or equivalent experience.
- 7+ years in a Data Engineering role.
- Proficiency in programming languages such as Python and SQL, managing large-scale data (Terabyte to Petabyte).
- Hands-on experience with big data technologies (Spark, Flink).
- Familiarity with machine learning frameworks (TensorFlow, PyTorch).
- Understanding of data warehousing concepts, ETL processes, and data modeling.
- Experience with API development and cloud platforms (Azure).
- Knowledge of DevOps practices and technologies such as Docker or Kubernetes.
- Experience in real-time/streaming data processing.
Technical Stack:
- Programming Languages: Python, SQL
- Big Data Technologies: Spark, Flink
- Databases: Clickhouse, MySQL, PostgreSQL, MongoDB, Cassandra, HBase, Redis
- Cloud Platforms: Azure
- API Development: RESTful APIs, GraphQL
- Data Services: Kafka, RabbitMQ
- Containers: Docker, Kubernetes
Language Requirements: Not specified.
