Data Engineer II - ETL/Data Warehousing

About Metayb :

Metayb is a fast-growing digital transformation company empowering organizations to thrive in the digital-first era.

In just three years, we've built a team of 500+ experts focused on creating seamless customer experiences, boosting operational efficiency, and delivering actionable insights.

Our core strengths include Digital Transformation, Data Science, SAP Implementation, Workflow Automation, Finance, and Supply Chain Services, with aspirations to expand into IoT, AI/ML, and Virtual Reality.

By leveraging emerging technologies, Metayb aims to be a trusted global partner in delivering impactful, future-ready solutions.

Role Overview :

As a Senior Data Engineer, you will be responsible for designing, developing, and maintaining scalable data solutions that enable business intelligence, analytics, and data-driven decision-making.

You will work closely with business stakeholders, data analysts, and technology teams to build robust data pipelines, optimize data architectures, and ensure the availability, quality, and security of enterprise data assets.

The ideal candidate will possess strong expertise in modern data engineering practices, cloud-based data platforms, and large-scale data processing frameworks, with hands-on experience in Microsoft Fabric and Azure data services.

Key Responsibilities : Data Engineering & Pipeline Development :

• Design, develop, and maintain scalable data pipelines and ETL/ELT processes to support analytics and reporting requirements.

• Build and optimize data ingestion frameworks for structured, semi-structured, and unstructured data sources.

• Develop efficient data models and architectures that support enterprise-wide reporting and analytics initiatives.

• Ensure data systems are scalable, secure, reliable, and capable of handling large volumes of data.

Data Architecture & Platform Management :

• Design and implement modern data architectures aligned with business and technology objectives.

• Manage and optimize cloud-based data platforms, data warehouses, and lakehouse environments.

• Collaborate with solution architects and business teams to translate requirements into scalable technical solutions.

• Drive best practices in data governance, security, and performance optimization.

Data Quality & Governance :

• Implement data quality checks, validation frameworks, and monitoring mechanisms to ensure data accuracy and integrity.

• Establish standards and processes for data management, lineage, and governance.