Data Engineer II

AI in this role

Job Purpose

Design and deliver robust, scalable data pipelines and infrastructure components that ensure reliable, high-quality data availability for analytics, data science, and AI workloads. The role operates with growing technical ownership, taking end-to-end responsibility for assigned platform domains and contributing meaningfully to architectural decisions and engineering standards.

Key Result Responsibilities

Design, develop, and maintain scalable ETL/ELT pipelines that ingest, transform, and serve data from structured and unstructured sources across cloud environments
Own assigned pipeline domains end-to-end — from requirements and design through to deployment, monitoring, and iterative improvement
Implement and evolve data models that support analytics, BI, and machine learning consumption requirements
Build and maintain orchestration workflows using tools such as Apache Airflow, dbt, or Azure Data Factory
Optimize pipeline and query performance across cloud data platforms including Snowflake and Azure Synapse
Define and implement data quality rules, automated testing, and alerting to ensure reliability and consistency of data outputs

Key Result Responsibilities-Continued

Contribute to architectural discussions and platform decisions, providing well-reasoned technical input and trade-off analysis
Collaborate with Data Scientists, Analytics Engineers, and business stakeholders to translate data requirements into maintainable engineering solutions
Conduct code reviews and support the development of Associate and DE I engineers through practical guidance
Maintain clear documentation for all assigned pipelines, data models, and infrastructure components

Qualifications (Academic, training, languages)

Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related field.
Fluent in English Language.
ITIL Certification is an advantage but not mandatory.
Strong proficiency in SQL and Python for data transformation, automation, and pipeline development.
Working knowledge of data orchestration and transformation tools (e.g., Apache Airflow, dbt).
Understanding of data modelling techniques — dimensional modelling, star/snowflake schema, and medallion/lakehouse architecture patterns.
Familiarity with batch and streaming data processing concepts (e.g., Spark, Kafka, Azure Event Hubs).
Good grasp of software engineering fundamentals: version control (Git), CI/CD practices, and automated testing.
Ability to communicate technical designs clearly and provide constructive feedback to junior team members.

Work Experience

With 4–6 years of hands-on experience in data engineering or a closely related software engineering role
Demonstrable experience delivering production-grade data pipelines in a cloud environment.
Solid hands-on experience with cloud data platforms on Azure, AWS, or GCP (e.g., Azure Data Factory, Synapse Analytics, S3, Redshift, BigQuery).
Experience with data warehousing platforms such as Snowflake or Azure Synapse, including query optimisation and access control.

How we rate this

Data Engineer II at Air Arabia rates 67 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

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