Senior Specialist, Data Engineering

About the company

Ooredoo is a global telecommunications group operating across 10 countries and supporting more than 138 million customers. In Qatar, the company has around 1,600 employees and is focused on being the preferred provider of advanced communication services.

Role overview

This position plays a key part in shaping the data foundation behind Ooredoo’s AI and digital transformation initiatives. The Senior Specialist, Data Engineering will design, build, and run scalable, well-governed data pipelines and data products on the company’s Google Cloud Platform (GCP) data environment, which acts as the main source of truth for operational, network, and analytical data. The role works closely with the data platform delivery partner, data science, and commercial teams to turn business and analytical needs into reusable production-grade data assets for analytics, reporting, and AI/ML use cases across customer value management, marketing, digital sales, and customer care. It also supports the controlled migration and integration of on-premises systems such as Teradata, Informatica, and Qlik into GCP.

Functional context

Ooredoo has a strong commitment to becoming a data-led organization. As the business environment evolves, there is growing focus on applying AI and machine learning in daily operations to improve value creation, efficiency, and diversification. The AI Hub division leads the data and AI roadmap, business plan, and strategy. The company is building cloud-based solutions on GCP for the data platform and analytics/ML workloads, while Azure supports GenAI and agentic AI workloads.

Key responsibilities

• Design and improve real-time analytical data stores and feature stores that help data scientists train, validate, and deploy models in DataIKU.
• Create strong ingestion, transformation, and enrichment workflows using real-time data products and ETL tools such as Informatica, advanced SQL, and Dataiku, with a focus on speed and accuracy.
• Work with data scientists to convert analytical and modeling requirements into reliable production data assets, reusable features, and standardized frameworks.
• Integrate datasets smoothly with reporting and BI tools such as SAP BusinessObjects and QlikSense, ensuring business users receive trusted curated data.
• Apply data quality controls, metadata practices, and validation frameworks to protect the reliability and governance of critical datasets.
• Automate recurring data workflows, pipeline operations, and monitoring to reduce manual effort and improve efficiency.
• Support platform performance through tuning, workload management, and effective use of enterprise data warehouse capabilities.
• Help define and deploy new engineering standards, coding practices, and reusable components across the organization.
• Maintain clear documentation for end-to-end data flows, data models, pipeline dependencies, and operational procedures in line with governance standards.
• Collaborate with Technology and Infrastructure teams to deliver secure, compliant, and scalable data engineering solutions.
• Contribute within a shared services model to support growth, cost efficiency, customer experience improvement, and digital transformation efforts.

Qualifications and experience

A bachelor’s degree in Computer Science, Engineering, or a related field is required. The role calls for around 10 years of experience in a similar position, with strong practical exposure to data engineering, ETL development, and enterprise data warehousing.

Technical background

The ideal candidate should have solid hands-on experience with GCP data engineering tools including BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Composer, and Cloud Storage for SQL transformation, testing, and lineage. Strong capability in Informatica for ETL/ELT workflow design, automation, and data quality is essential. The role also requires deep understanding of analytical data stores, feature stores, and curated datasets used by analysts and data scientists, along with advanced SQL and Python skills.

Experience in designing consumption layers and feature stores, supported by strong data modeling knowledge such as dimensional modeling, star schema design, and third normal form, is important. Exposure to BI and reporting platforms such as SAP BusinessObjects, QlikSense, and preferably Looker is also expected. Knowledge of governance, metadata management, lineage, data quality, privacy, and security is needed, and familiarity with feature stores, vector stores, and pipelines used for AI/ML and GenAI use cases such as RAG will be considered an advantage.

Communication and working style

Strong communication skills and stakeholder management ability are important, especially the ability to explain technical topics in clear business language.