Data Architect; Category - Architect

Data Architect (Category - Architect)

Sector:
Oil and Gas

Location:

Doha, Qatar

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.

• 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.

• Architect cloud-native data platforms using Azure (ADF, Synapse, Databricks, Fabric) and/or GCP equivalents.
• Design secure hybrid architectures integrating on‑premises systems with cloud data platforms.

• Define and enforce data governance frameworks, including metadata management, data lineage, data ownership, and stewardship models.
• Establish data quality standards, validation rules, and monitoring mechanisms.
• Ensure compliance with ISO 27001, NIA Qatar, data privacy, and internal cybersecurity policies.
• Define data security architecture including encryption, access control, and role‑based data access.

• Design data architectures that support advanced analytics, AI/ML, and digital twin initiatives.
• Collaborate with data scientists and analysts to optimize data models for analytics and AI consumption.

• Establish data architecture standards, reference architectures, and best practices.
• Review solution designs to ensure alignment with enterprise data architecture principles.

• Bachelor’s or master’s degree in computer science, Data Engineering, Information Systems, or related field.
• 10+ years of experience in data architecture, data engineering, or enterprise analytics roles.
• Strong experience designing enterprise‑scale data platforms in cloud and hybrid environments.
• Expertise in data modelling (conceptual, logical, physical) and data integration patterns.
• Deep understanding of data lakes, data warehouses, Lakehouse architectures, and real‑time streaming platforms.
• Hands‑on experience with cloud data services (Azure preferred).
• Strong knowledge of SQL, data transformation frameworks, and metadata management.
• Familiarity with Dev Ops and Data Ops practices.
• Strong communication skills with the ability to engage both technical and business stakeholders.

• 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 life cycles 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., Postgre
  
  SQL, SQL Server) and No
  
  SQL 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.