Data Governance & AI Architect

Responsibilities

• Design and evolve modern data architectures including data lakehouses, data mesh, and real-time streaming systems.
• Lead the development of scalable data pipelines and integration frameworks using tools like Databricks, Spark, Airflow, and Kafka.
• Define data modeling standards and ensure alignment with governance, privacy, and compliance requirements.
• Optimize data infrastructure for performance, reliability, and cost-efficiency across cloud platforms.
• Collaborate with data governance teams to embed policies, standards, and controls into data and AI architecture.
• Ensure data lineage, metadata management, and data quality frameworks are integrated into platform design.
• Support regulatory compliance and internal governance initiatives.
• Promote data stewardship and ownership models across business and technical domains.
• Architect end-to-end AI/ML platforms, including feature stores, model training environments, deployment pipelines, and monitoring systems.
• Implement MLOps best practices for model lifecycle management, CI/CD, and performance tracking.
• Ensure AI systems are designed for scalability, explainability, and ethical use.
• Integrate AI solutions with cloud-native services (e.g., Azure ML, AWS SageMaker, GCP Vertex AI).
• Partner with cybersecurity and legal teams to ensure data and AI systems meet security and ethical standards.
• Implement controls for data access, encryption, and model transparency.
• Support responsible AI practices including bias detection, fairness, and accountability.
• Work cross-functionally with analytics, governance, security, and business teams to align architecture with strategic goals.
• Mentor data engineers and ML engineers, promoting engineering excellence and architectural best practices.
• Evaluate emerging technologies and trends to inform innovation and investment.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, AI, or related field.
• 8+ years of experience in data engineering, architecture, and AI systems design.
• Proven experience building and scaling enterprise data and AI platforms with governance and security integration.
• Deep expertise in cloud platforms (Azure, AWS, GCP), big data technologies, and ML frameworks.
• Strong understanding of data governance, privacy regulations, and responsible AI principles.
• Excellent communication, stakeholder engagement, and strategic planning skills.