Enterprise Data Architect

Role summary / purpose

Develops and implements AI solutions to enhance automation, predictive analytics, and operational efficiency. Integrates machine learning models to optimize decision-making and improve the reliability of power delivery systems. Works closely with engineering and operational teams to customize AI tools, ensuring tailored solutions that address specific project and business challenges.

Key responsibilities

• Define and lead the enterprise data architecture strategy, standards, and roadmap across data platforms, analytics, AI, and cloud ecosystems.
• Architect scalable enterprise data platforms using Databricks Lakehouse, Delta Lake, Unity Catalog, and cloud-native technologies to support business intelligence, analytics, machine learning, and AI use cases.
• Design and implement modern data architectures including data lakes, data warehouses, data mesh, streaming, and real-time data integration patterns.
• Establish enterprise data governance, metadata management, lineage, security, privacy, and access control standards across structured and unstructured data assets.
• Lead the architecture and optimization of data ingestion, transformation, and data product pipelines using Databricks, Spark, and cloud-native services.
• Develop enterprise data models, canonical data structures, and integration patterns that promote data consistency, interoperability, and reuse across business domains.
• Drive the adoption of DataOps, MLOps, and modern engineering practices to improve scalability, reliability, and operational efficiency of enterprise data platforms.
• Partner with business stakeholders, data product teams, enterprise architects, cybersecurity, and governance organizations to align data initiatives with business strategy and regulatory requirements.
• Evaluate and recommend emerging technologies, tools, and architectural patterns that enhance enterprise data and analytics capabilities.
• Architect data foundations that enable advanced analytics, machine learning, Generative AI, Agentic AI, and intelligent automation use cases.
• Design and govern enterprise AI data patterns including vector search, Retrieval Augmented Generation (RAG), semantic data access, and knowledge management architectures.
• Establish architecture standards for AI/ML platforms including model lifecycle management, observability, governance, evaluation, and deployment practices.
• Lead cross-functional architecture reviews and technology modernization initiatives to improve data quality, platform performance, and solution scalability.
• Mentor data engineers, analytics engineers, data scientists, and solution architects while promoting best practices in enterprise data architecture and AI enablement.
• Communicate architectural vision, business value, risks, and strategic recommendations to technology leadership and executive stakeholders.

Requirements - education & experience

• Master's or PhD in AI, Data Science, Engineering, or related field preferred.
• 8+ years of experience in Data Architecture, Data Engineering, Analytics Architecture, or Enterprise Architecture.

Required skills

• Strong hands-on experience with Databricks, Spark, Delta Lake, Unity Catalog, and MLflow.
• Experience with modern cloud data platforms.
• Enterprise data governance and architecture design.

Nice-to-have / preferred skills

• Experience designing enterprise-scale data modernization and migration programs.
• Working knowledge of Generative AI, Agentic AI, RAG, and Vector Databases.
• Experience with AI platform architectures.
• Experience working with Azure, AWS, or GCP cloud ecosystems and enterprise governance frameworks.