Drive adoption of modern engineering ways of working on Azure-based data platforms, ensuring reliability, performance, security, and operational readiness at scale
Strengthen the enterprise data foundation that enables trusted analytics, AI, and data products, supporting better decisions for FrieslandCampina’s colleagues, customers, and member farmers
Set the global direction for data engineering, defining architecture principles, standards, and best practices for data pipelines, platforms, and API-based data consumption
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Drive adoption of modern engineering ways of working on Azure-based data platforms, ensuring reliability, performance, security, and operational readiness at scale
Strengthen the enterprise data foundation that enables trusted analytics, AI, and data products, supporting better decisions for FrieslandCampina’s colleagues, customers, and member farmers
Set the global direction for data engineering, defining architecture principles, standards, and best practices for data pipelines, platforms, and API-based data consumption
...
Drive adoption of modern engineering ways of working on Azure-based data platforms, ensuring reliability, performance, security, and operational readiness at scale
Strengthen the enterprise data foundation that enables trusted analytics, AI, and data products, supporting better decisions for FrieslandCampina’s colleagues, customers, and member farmers
Set the global direction for data engineering, defining architecture principles, standards, and best practices for data pipelines, platforms, and API-based data consumption
...
Full Stack EngineeringFrontend: React, Next.js, TypeScript, modern component architectures, state management, real-time and streaming AI interfaces, agent activity and execution interfaces, data visualization.Backend: Node.js, TypeScript, Python, REST APIs, GraphQL, WebSockets and streaming, event-driven architectures, background workers, job queues, distributed systems, authentication and authorization.
Distributed SystemsDesign systems that reliably execute thousands or millions of AI and data-processing tasks. Kubernetes, Docker, Cloud Run and serverless, message queues, Redis, Kafka or equivalent, distributed job processing, concurrency management, rate limiting, retries, idempotency, fault tolerance, observability. You know how to build systems that stay reliable when agents fail, APIs time out, models hallucinate, or downstream services go away.
Data & Learning InfrastructureBuild the infrastructure agents need to learn from historical executions. PostgreSQL, BigQuery or equivalent data warehouses, ClickHouse or analytical databases, vector databases, embeddings, retrieval systems, event logs, feature stores, analytics pipelines, data ingestion.
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