Job Description
Data, Backend and AI Services - Enterprise Data Platform and KMS
As part of our client's engineering organization, we are looking for Agentic Software Engineers to build and ship features across the data platform and the Knowledge Management Service. This is the core engineering role in our pod model and the one we hire most of.
The role is agent-first. You direct AI coding agents to generate code, data transformations and tests, then review that output critically and own the result in production. The engineering judgment is yours regardless of who or what typed the code.
This is not a prompt-writing position, and it is not a conventional feature-factory engineering job either. You will spend more time specifying precisely, reviewing rigorously and testing than writing lines by hand. Candidates who want to be measured on hand-written output will find the role frustrating.
We hire this role at several levels, from associate through senior engineer. Scope, independence and the expectation to mentor others increase with level, but the agent-first way of working does not change.
Key Responsibilities:
- Build and ship features across the platform: ingestion, transformation, APIs, backend services and data models.
- Direct AI agents to generate code, transformations and tests, then review their output for correctness, performance, security and maintainability.
- Write clear, testable specifications that both agents and engineers can build from without guessing.
- Own your slice end to end: build it, test it, release it through CI/CD and support it in production.
- Keep work flowing on a Kanban board in small increments with tight work-in-progress limits.
- Review your pod peers' work and raise the engineering standard rather than simply approving pull requests.
- Contribute to the pod's technical decisions and to platform-wide engineering standards.
About You:
The ideal candidate is a capable engineer who is genuinely energised by working agent-first rather than merely tolerant of it. They write specifications that remove ambiguity, they read generated code with suspicion, and they take responsibility for what reaches production. They are not a passive consumer of AI output and not an engineer who needs a ticket broken down for them. They take a problem, specify it, direct the work, verify it and ship it.
Requirements
- Solid professional software or data engineering experience, with code you have shipped and then supported in production.
- Strong coding ability in at least one of Python, Java, C#, Scala or TypeScript, plus confident SQL.
- Experience with data pipelines or distributed data processing such as Spark, PySpark, dbt, Azure Data Factory or Databricks workflows.
- Sound grasp of data modelling, API design and the trade-offs between them.
- Strong backend engineering experience building and operating server-side services at enterprise scale.
- Java and Spring Boot are required for the platform backend positions; candidates for those positions must have production Java depth rather than familiarity.
- REST API design, including versioning, error semantics, authentication and backwards compatibility.
- Ability to diagnose and fix latency, concurrency and memory problems in a running service.
- Working experience building and running services on Microsoft Azure.
- Familiarity with storage, identity and access management, secrets handling and basic networking.
- Understanding of cost and performance consequences of design choices at enterprise scale.
- Fluency with Git-based workflows, pull requests, automated testing and CI/CD.
- Ability to write tests that establish confidence rather than coverage percentages.
- Habit of small, reviewable increments over large batched changes.
- Strong GitHub experience including Actions, repository management, branching strategies, pull request workflows and automation.
- Experience working in Git-based development models across multiple teams.
- Practical experience directing AI coding agents such as Factory.ai or GitHub Copilot to produce production code, and reviewing that output critically rather than accepting it.
- Ability to write precise, testable specifications that an agent can build from without coming back for clarification.
- Sound judgment about where agent autonomy is appropriate and where a human decision is mandatory.
- Comfort being measured on agent autonomy coverage and rework rate alongside conventional delivery measures.
- Preferred: Microsoft Fabric, Snowflake or MongoDB Atlas alongside Databricks.
- Preferred: Streaming and event-driven data work with Kafka or Azure Event Hubs.
- Preferred: Building LLM or retrieval-augmented generation applications, including retrieval quality evaluation.
- Preferred: Containerised deployment with Docker and Kubernetes.
- Preferred: Infrastructure as code with Terraform or Bicep.
- Preferred: Experience in global, distributed engineering organizations.
Benefits
- Pods of five with end-to-end ownership. Your pod builds, tests, releases and runs its own slice of the platform. Nothing is handed to a separate QA or operations function.
- Kanban and flow rather than sprints. Work is pulled, work in progress is limited, and throughput, lead time and cycle time are measured. There is no sprint commitment ceremony and no PI planning.
- A six-phase product lifecycle: Plan, Define, Build, Review, Release and Support.
- Tooling: GitHub for Projects, Issues, Actions and GitOps; Factory.ai and GitHub Copilot for agentic engineering; Nullplatform for environments; Aha for portfolio intake.
- Agent-first delivery. We target 60 to 70 percent agent autonomy on eligible work and rework below 15 percent, which shifts engineering effort from typing code to specifying and reviewing it.