Data Platform Engineer (AI Integrations)
Role details
Job location
Tech stack
Job description
RWS is building the next generation of AI-enabled capabilities across our products, internal production systems, and enterprise platforms. To accelerate this effort, we're establishing a small, focused team to deliver the foundational integrations that will allow Agentspace, Gemini Enterprise, and other AI workflow engines to interact securely and intelligently with our core systems.
As a Data Platform Engineer (AI Enablement), you will design and deliver the data access patterns, ingestion workflows, and lightweight data products that allow Agentspace, Gemini Enterprise, and our emerging AI orchestration layer to operate with high-quality, well-structured information. You will work closely with various technical roles and domain experts across the business to turn AI use cases into real, production-ready capabilities.
This is a hands-on contract role requiring strong engineering fundamentals, a deep understanding of cloud data platforms, and a practical mindset toward shaping new patterns as we scale AI adoption across RWS. About Product & Technology Product & Technology plays a pivotal role in aligning the organization with its strategic objectives and enhancing shareholder value. Product & Technology is responsible for establishing unified standards and governance practices throughout the company. Additionally, we oversee the development and maintenance of core applications essential for the seamless operation of various functions across the organization. We are committed to driving and executing future roadmaps that are in line with the overall strategic direction of RWS. With a global reach, Product & Technology provides support services to over 7500 end users worldwide. We take pride in managing the information security operation and safeguarding all our assets. Our core functions encompass Enterprise & Technical Architecture, Network & Voice, Infrastructure, Service Delivery, Service Operations, Data & Analytics, Security & Quality Compliance, Transformation, Application Development, Enterprise Platforms, With a dedicated team of over 500 staff, Product & Technology ensures a strong presence across all regions, enabling efficient and effective support to our global operations. Job Overview: Key Responsibilities Your work will focus on making curated RWS data accessible to AI agents in a secure, efficient, and well-structured way. Typical areas of focus include:
- Building lightweight ingestion or sync pipelines to bring priority datasets from various source systems into platforms that enable AI use.
- Working with data experts and domain teams to understand existing curated datasets and expose them safely to the AI platform.
- Designing simple, reliable data-access APIs or query layers that agents can call when performing tasks.
- Structuring data for agent use: transforming, organising, or indexing information so AI workflows can retrieve and reason over it effectively.
- Supporting early retrieval/RAG-style patterns, including chunking, metadata schemas, and optimisation of search/retrieval performance.
- Implementing access controls, lineage awareness, and governance patterns aligned with enterprise data policy.
- Collaborating with other team members to support end-to-end agent workflows that combine system integrations with structured or semi-structured data access.
Requirements
- Strong experience building data pipelines and ingestion workflows (batch or streaming) in a cloud environment (preferably GCP).
- Hands-on experience with BigQuery or similar analytical data platforms.
- Ability to design data access APIs, SQL interfaces, or intermediate layers for downstream systems.
- Familiarity with data modelling concepts and the ability to work with curated datasets or semantic layers.
- Understanding of secure data access patterns, IAM, service accounts, and governance best practices.
- Comfort working with unstructured, semi-structured, or document-based sources when needed to support retrieval workflows.
- A pragmatic mindset: knowing when to build a "quick path" for a prototype versus shaping a more robust reusable pattern.
- Strong communication and the ability to collaborate across product, engineering, and data organisations.
- Experience with AI retrieval, embeddings, or RAG concepts is beneficial.
Benefits & conditions
- Contract
- Published: 13 hours ago
- Competitive