Software Engineer, Data & AI Platforms

Design and build backend software and data services that turn scattered enterprise and operational data into well-structured, well-governed, easily consumable products. The engineer will work across data modelling, pipelines, APIs and platform services, and will be expected to take a design from concept to a production-grade, reusable implementation with experimentation and iteration approach.

Responsibilities

• Design and build backend services, data models and APIs that are clean, documented, testable and reusable across multiple deployments.
• Develop data pipelines for batch, streaming and unstructured sources; handle ingestion, transformation, quality checks and historisation.
• Model data thoughtfully relational, dimensional, graph or semantic choosing the representation that fits the problem rather than a default.
• Build metadata, catalog and discovery layers so that datasets and services are self-describing: schema, units, granularity, ownership, lineage and access method available programmatically.
• Expose data and capabilities through well-specified interfaces (REST or equivalent, tool/function interfaces for AI agents) with authentication, authorisation and versioning built in.
• Engineer for production: CI/CD, infrastructure-as-code, observability, testing, performance tuning and cost awareness.
• Convert working prototypes into reusable blueprints templates, reference schemas and deployment patterns that other teams can instantiate.
• Partner with domain, business and platform stakeholders to translate requirements into technical design, and document decisions clearly.
• Good to have: Exposure to industrial or operational data sensor/time-series, alarms and events, maintenance and work-order systems, quality data, engineering documents and to plant asset hierarchies.
• Experience with knowledge graphs, ontologies or semantic modelling.
• Experience grounding LLM or agentic applications in enterprise data, including retrieval, document indexing and tool/API interfaces for agents.
• Prior work standardising a solution across multiple sites, plants or business units.

Requirements

• Strong software engineering fundamentals: data structures, API and system design, modular code, version control, code review, automated testing.
• Proficiency in Python and SQL
• Hands-on experience with a modern cloud data platform or lakehouse, and with distributed processing and streaming frameworks.
• Platform-agnostic design ability matters more than any single product.
• Working knowledge of databases across paradigms relational, document, time-series, graph and vector and the judgement to pick between them.
• Experience with containers, orchestration and CI/CD pipelines in a cloud environment.
• Familiarity with data governance concepts: identifiers and master data, access control, lineage, schema evolution and data quality.
• Clear written and verbal communication; able to document architecture and defend design choices.

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