Forward Deployed Engineer - Data Engineer

Who You Are

Required skills and experience •Programming Proficiency: Demonstrable expertise in Python, (or another modern language such as C#, Node.js or TypeScript); experience working with AI/ML frameworks like TensorFlow or PyTorch is a plus •Software Engineering: Solid grasp of the software delivery lifecycle, version control (Git & GitHub), and data engineering tools such as Pandas and Spark •Cloud & Distributed Systems: Experience with cloud AI platforms (AWS, Azure, Google AI) and distributed computing architectures Preferred skills and experience •Open-Source Ecosystems: Familiarity with community-driven AI tools and libraries, including Hugging Face and relevant repositories •T-shaped Profile: Deep technical expertise in one or two domains, with broad understanding across AI/ML, cloud, and consulting •Agentic AI Systems: Experience designing, building, or integrating multi-agent systems and orchestration frameworks (e.g., LangGraph, Semantic Kernel, Agent Framework, AutoGen, CrewAI), including the development of agent protocols and coordination mechanisms •Retrieval-Augmented Generation (RAG) & Knowledge Systems: Practical knowledge of deploying RAG architectures and integrating structured and unstructured knowledge sources into AI solutions •Performance & Security: Knowledge of system-level optimisation and security best practices for scalable AI systems •Business Acumen: Ability to translate business requirements into technical solutions and communicate technical value to diverse stakeholders, including executive audiences •Flexibility: Willingness to travel and work on customer premises as required •Education: Degree-level qualification in Computer Science, Informatics, Data Science, Engineering, or a closely related discipline - or equivalent professional experience

The Role

At Kyndryl, Forward Deployed Engineers work where data, cloud, and AI become real. This is not a traditional consulting role or a back-office engineering position. Forward Deployed Engineers embed directly with customers to design, build, and deploy modern data solutions that operate in complex, regulated, production environments.

As a Forward Deployed Engineer (FDE) - Data Engineering & GCP, you will own defined delivery workstreams within customer engagements, designing and building scalable data platforms, pipelines, and data products primarily on Google Cloud Platform (GCP).

You will operate with growing independence in customer environments, combining strong hands-on Data Engineering expertise with business context. You will translate ambiguous business and data challenges into robust, production-ready solutions, from rapid prototypes and MVPs through industrialization and production deployment.

The role sits at the intersection of Data Engineering, Cloud Architecture, AI enablement, consulting, and delivery accountability. You will work across the data lifecycle-from ingestion and transformation to orchestration, governance, observability, and data serving-creating trusted data foundations for analytics, operational workloads, Machine Learning, and Generative AI.

You will use modern engineering and AI-assisted development tools to accelerate solution design, coding, testing, troubleshooting, and deployment, while building the customer trust required to drive adoption and long-term impact.

• Customer Engagement & Solution Engineering: Partner directly with customers to understand business and data challenges and translate them into high-quality, fit-for-purpose technical solutions, balancing delivery speed with scalability, maintainability, security, and long-term architectural integrity.
• Data Engineering on GCP: Design, build, and optimize scalable data pipelines and data platforms using technologies such as BigQuery, Cloud Storage, Dataflow, Dataproc, Pub/Sub, Cloud Composer/Airflow, and Dataform, selecting the right services and patterns for each workload.
• Data Architecture & Modernization: Contribute to the design and implementation of modern cloud data architectures, including data lakes, data warehouses, lakehouse patterns, batch and streaming processing, and migrations from legacy or on-premises data platforms to GCP.
• Rapid Prototyping: Build and iterate PoCs, MVPs, and production-oriented prototypes to validate architecture decisions, integration patterns, performance, and business value quickly with real customer data and environments.
• Ownership, Deployment & Partnership: Own delivery workstreams end to end-from technical discovery and solution design through implementation, testing, deployment, and production stabilization. Work as part of the customer team rather than operating only as an external advisor.
• Data Integration & Processing: Implement robust batch and real-time ingestion, transformation, and processing patterns across heterogeneous data sources, APIs, databases, files, event streams, and enterprise platforms.
• Production Engineering: Build solutions with production requirements in mind, including CI/CD, Infrastructure as Code, automated testing, monitoring, observability, security, IAM, data quality, lineage, and operational supportability.
• Performance & Cost Optimization: Troubleshoot and optimize data workloads for performance, scalability, reliability, and cost, particularly across BigQuery, Spark/Dataproc, Dataflow, and other GCP data services.
• AI & ML Data Enablement: Build and integrate the data foundations required for Machine Learning and Generative AI solutions, including reliable data pipelines, feature preparation, data quality, vector and unstructured data processing, and integration with GCP AI services such as Vertex AI.
• Field Intelligence: Systematically capture deployment learnings, reusable patterns, technical accelerators, and best practices from customer engagements and share them across Kyndryl engineering and delivery teams.
• Continuous Learning: Stay current with the evolving GCP data ecosystem, modern Data Engineering practices, AI-assisted engineering, and emerging technologies, applying new capabilities pragmatically to customer problems.
• Platform & Engineering Contribution: Contribute reusable code, reference architectures, automation, frameworks, and feedback that improve Kyndryl's Data & AI engineering capabilities and accelerate future customer engagements.