Lead Data architects lead the design and implementation of data collection, storage, transformation, orchestration (movement) and consumption to achieve optimum value from data. They are the technical leaders within data delivery teams. They play a key role in modelling data for optimal reuse, interoperability, security, and accessibility as well as in the design of efficient ingestion and transformation pipelines. They ensure data accessibility through a performant, cost-effective consumption layer that supports use by citizen developers, data scientists, AI, and application integration. And they instill trust through the employment of data quality frameworks and tools.

The data architect at Chevron predominantly works within the Azure Data Analytics Platform, but they are not limited to it. The Senior Data architect is responsible for optimizing costs for delivering data. They are also responsible for ensuring compliance to enterprise standards and are expected to contribute to the evolution of those standards resulting from changing technologies and best practices.

Key Responsibilities:

• Design and overseeing the entire data architecture strategy.
• Mentor junior data architects to ensure skill development in alignment with the team strategy.
• Design and implement complex scalable, high-performance data architectures that meet business requirements.
• Model data for optimal reuse, interoperability, security, and accessibility.
• Develop and maintain data flow diagrams, and data dictionaries.
• Collaborate with stakeholders to understand data needs and translate them into technical solutions.
• Ensure data accessibility through a performant, cost-effective consumption layer that supports use by citizen developers, data scientists, AI, and application integration.
• Ensure data quality, integrity, and security across all data systems.

Qualifications:

• Experience in Erwin, Azure Synapse, Azure Databricks, Azure DevOps, SQL, Power BI, Spark, Python, R.
• Ability to drive business results by building optimal cost data landscapes.
• Familiarity with Azure AI/ML Services, Azure Analytics: Event Hub, Azure Stream Analytics, Scripting: Ansible
• Experience with machine learning and advanced analytics.
• Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes).
• Understanding of CI/CD pipelines and automated testing frameworks.
• Certifications such as AWS Certified Solutions Architect , IBM certified data architect or similar are a plus.