Senior Specialist Data Engineering

About Us:

Ooredoo is a dynamic global Telecommunications player operating in 10 countries serving more than 138 million customers. Ooredoo Qatar employs approximately 1,600 people driving Ooredoo to be the number one choice for world-class communications services in Qatar, and it is a team that you can be part of!

About the Role:

This role is engineering the data foundation that powers Ooredoo's AI and digital transformation agenda. The Senior Specialist, Data Engineering designs, builds, and operates scalable, governed data pipelines and data products on Ooredoo's Google Cloud Platform (GCP) data platform, the

program's primary data layer and the source of truth for operational, network, and analytical data. Working alongside the data platform delivery partner, data science, and commercial teams, the role translates business and analytical needs into production-ready, reusable data products that serve

analytics, reporting, and AI/ML workloads across customer value management (CVM), marketing, digital sales, and customer care — while ensuring on-premises sources (Teradata, Informatica, Qlik) are reliably integrated and progressively migrated to GCP.

Functional Context:

Ooredoo places strong emphasis on a data-driven culture. In an ever-changing business landscape, there is increasing organizational focus on using AI/ML in day-to-day practice to create value, efficiency, and diversification.

The AI Hub division is responsible for putting in place and executing the data & AI roadmap, business plan, and strategy. Ooredoo is building cloud platform-based solutions involving GCP that hosts the data platform and supports analytics and ML workloads, while Azure hosts GenAI and agentic AI workloads.

Role Accountabilities:

• Develop and optimize real time analytical data stores and feature stores to enable data scientists to efficiently train, validate, and deploy models in DataIKU.

• Build robust data ingestion, transformation, and enrichment processes built on real time data products using ETL tools e.g.

• Informatica, advanced SQL scripting or Dataiku ensuring high performance and accuracy.

• Collaborate with data scientists to translate analytical and modeling needs into production ready data assets, features, and reusable data frameworks.

• Ensure seamless integration of datasets with reporting and BI platforms, including SAP BusinessObjects and QlikSense, providing curated, trusted datasets.

• Implement data quality rules, metadata management, and validation frameworks to ensure reliability and governance of mission critical datasets.

• Automate repeated data processes, pipelines, and monitoring systems to minimize manual intervention and improve efficiency.

• Support data platform optimization through performance tuning, workload management, and efficient use of enterprise DWH capabilities.

• Participate in the design and rollout of new data engineering standards, coding guidelines, and reusable components across the organization.

• Document end to end data flows, data models, pipeline dependencies, and operational processes in alignment with governance best practices.

• Partner with Technology and Infrastructure teams to ensure secure, compliant, and scalable deployment of data engineering solutions.

• Work within a shared function to support commercial growth, cost optimization, customer experience enhancements, and digital transformation initiatives.

Minimum Entry Qualifications:

Bachelor’s Degree in Computer Science, Engineering or Similar

Minimum Experience, Essential Knowledge & Skills:

10 years of experience in a similar role.

Prior experience in data engineering, ETL development, and enterprise data warehousing.

Strong hands-on GCP data engineering — BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Composer, and Cloud Storage for SQL transformation, testing, and lineage.

Expertise in Informatica for ETL/ELT workflow design, automation, and data quality.

Deep understanding of building analytical data stores, feature stores, and curated datasets used by

data scientists and analysts.

Strong command of advanced SQL (window functions, aggregations, complex transformations) and Python for data engineering.

Experience designing curated, and consumption layers and feature stores, with sound data-modelling skills (dimensional modelling, star schema, third normal form).

Experience supporting BI/reporting platforms such as SAP BusinessObjects, and QlikSense, including data modeling for consumption layers.

Experience supporting BI and consumption platforms e.g, Looker (preferred), SAP BusinessObjects, and QlikSense — including data modelling for consumption layers.

Understanding of data governance, metadata management, lineage, data quality, privacy, and

security principles.

Awareness of feature stores, vector stores, and data pipelines that support AI/ML and GenAI (e.g., RAG) use cases is an advantage.

Strong communication and stakeholder-management skills, with the ability to explain technical concepts in business-friendly language