Enterprise IT Architect - Data - kyndryl

At Kyndryl, we run and reimagine the mission-critical technology systems that drive advantage for the world’s leading businesses. We are at the heart of progress; with proven expertise and a continuous flow of AI-powered insight, enabling smarter decisions, faster innovation, and a lasting competitive edge. For our people—Kyndryls—that means doing purposeful work that powers human progress. Join us and experience a flexible, supportive environment where your well-being is prioritized and your potential can thrive.

The Role

Are you an experienced Enterprise IT Architect looking for an exciting opportunity to shape the future of software development? Look no further than Kyndryl, where you'll have the chance to make a meaningful impact as you lead the design and development of cutting-edge software applications. As a Software Engineering Architect at Kyndryl, you will be responsible for leading the charge in creating technical blueprints, defining product/solution architecture, and developing the technical specifications for our projects. You'll be a key player in integrating the features of our software applications into a cohesive and functioning system.

This role is responsible for shaping and owning the enterprise data platform architecture across on-premises, hybrid, and cloud environments. It focuses on modernizing and consolidating data engineering, analytics, BI, data science, and AI/ML platforms while establishing scalable, secure, governed, and reusable architecture patterns. The role also enables a future-ready data foundation to support Dataiku-based analytics, AI/ML workflows, Agentic AI use cases, and regulatory reporting.

Key Responsibilities

Define enterprise data platform strategy, target-state architecture, roadmaps, standards, and reusable reference patterns.

Rationalize and modernize legacy/overlapping tools such as SAP BODS, Oracle Data Integrator, BusinessObjects, Qlik, Power BI, MicroStrategy, and multiple Dataiku instances.

Architect robust data pipelines for batch, API-based, event-driven, and cloud-native data integration.

Design scalable data engineering patterns using SQL, Python, ETL/ELT, APIs, data warehouse, data lake, and cloud-native services.

Enable data science and AI/ML workflows including Dataiku, model lifecycle, feature engineering, model monitoring, traceability, and explainability.

Define architecture for governed Agentic AI capabilities that consume trusted enterprise data and integrate with business/workflow systems.

Establish data governance standards for data quality, lineage, metadata, access control, privacy, retention, auditability, and compliance.

Drive cloud adoption through Azure landing zone-aligned data architecture, Azure-native monitoring, automation, and security controls.

Embed DevSecOps, CI/CD, Infrastructure as Code, golden paths, and self-service patterns for data platform delivery.