Data Architect (Category - Architect) (Doha)

At Codvo, we are committed to building scalable, future-ready data platforms that power business impact. We believe in a culture of innovation, collaboration, and growth, where engineers can experiment, learn, and thrive. Join us to be part of a team that solves complex data challenges with creativity and cutting-edge technology.

Our digital transformation is moving from strategy to execution. We have a clear roadmap of high-impact data and AI use cases designed to fundamentally re-engineer our operations—from improving efficiency and implementing predictive maintenance on critical assets to enhancing subsurface modeling for better exploration outcomes. This is not just an IT upgrade; it is a core business imperative to create a more resilient, efficient, and intelligent energy company. We are now building the core team of innovators and builders to deliver these initiatives. This role is critical to bringing that vision to life, moving beyond theory to engineer the specific, robust data solutions that will power our most strategic projects and deliver measurable value.

The Data Architect will be responsible for designing, governing, and evolving the organization’s enterprise data architecture, ensuring that data platforms are scalable, secure, interoperable, and AI-ready. This role focuses on defining data models, integration patterns, governance standards, and architecture blueprints that enable analytics, AI, and digital products at scale.

The architect will work closely with data engineers, solution architects, cloud teams, business stakeholders, and cybersecurity teams to ensure data architecture aligns with business priorities and regulatory requirements.

Enterprise Data Architecture & Design:

•            Define and maintain the enterprise data architecture blueprint, including data lakes, warehouses, streaming platforms, and analytics layers.

•            Design logical, physical, and conceptual data models to support operational, analytical, and AI use cases.

•            Ensure architecture supports scalability, performance, and high availability.

•            Expert-level proficiency in SQL and Python for data manipulation and pipeline development.

•            Big Data Technologies: Hands-on experience with distributed computing frameworks like Apache Spark (PySpark). Experience with streaming technologies like Kafka is a plus.

•            Cloud Platforms: Deep experience with Microsoft Azure (Azure Data Lake Storage, Azure Data Factory, Azure Databricks, Azure Synapse).

•            Data Warehousing/Lakehouse: Proven experience with modern data platforms Databricks Delta Lake.

•            AI & ML Knowledge: Understanding of machine learning lifecycles and the data requirements for training and deploying AI/ML models.

•            Orchestration: Experience with workflow orchestration tools like Airflow, Dagster, or Azure Data Factory.

•            Databases: Strong understanding of both relational (e.g., PostgreSQL, SQL Server) and NoSQL databases. Experience with graph databases (e.g., Neo4j) and vector databases is highly desirable.

•            Version Control: Proficiency with Git and CI/CD best practices.

•            Certifications such as Azure Data Engineer Expert, Azure Solutions Architect, or equivalent.

•            Hands-on experience with the OSDU™ Data Platform.

•            Experience in Oil & Gas, energy, or industrial data environments.

•            Understanding of cybersecurity considerations for OT environments and data segregation.

•            Experience integrating data from ERP systems like SAP.

•            Experience working within AI-first or analytics-driven transformation programs.

•            Advanced SQL skills, including query optimization and performance tuning.

•            Knowledge of containerization technologies (Docker, Kubernetes).

•            Experience of constructing and maintaining enterprise knowledge graphs.