Engineering Manager - Data & AI

4 days ago Be among the first 25 applicants

Get AI-powered advice on this job and more exclusive features.

As a leading group of companies, the ECA International Group stands as a global frontrunner in simplifying international mobility. Our collective vision is to make a positive impact by delivering exceptional products and services to our prestigious list of large enterprise clients. Our global presence across the UK, EU, Hong Kong, Australia, and the US offers a world of opportunities, and our commitment to innovation ensures that you will be at the leading edge of your field.

We invest in people’s success and development pathways, creating a diverse and inclusive community where your unique talents shine. You will have a global impact and a work‑life balance, with flexibility to perform your best.

This role is ideal for someone who wants to shape how a modern enterprise becomes truly AI-native. We already use AI in delivery and engineering, and we have strong guardrails and governance in place. Now, we’re looking for a leader who can help us scale across data acquisition, ingestion, storage, and AI-powered consumption within a secure, multi-tenant environment. You’ll evolve our existing data platform (AWS, Snowflake, S3, Postgres, event‑driven services) into a product‑facing, AI‑ready foundation, while partnering across disciplines and leading a team in a supportive, high‑trust environment.

• Data platform evolution:
  Take our current AWS/Snowflake/S3/Postgres setup and enable AI/RAG, product consumption, and multi‑tenant access.
• Data acquisition & ingestion:
  Design multi‑source ingestion (APIs, scraping/crawling, file drops, crowd sourcing, agent‑based pipelines). Make it observable, repeatable, and documented so research and analytics can plug in new sources without rework.
• AI as a consumption layer:
  Build LLM/RAG endpoints on top of our data and content as a service. Expose these services to Data Insights to enable users to ask, explore, and generate insights – not only download reports.
• Partnering with Analytics:
  Give the research teams clean, modelled, well‑documented data. Turn one‑off work into scheduled, production jobs.
• Guardrails, governance, and quality:
  Keep AI‑generated code within SDLC, code review, and security bounds. Ensure data and AI services are audited and tenant aware.
• Team leadership:
  Lead a small technical team (data/AI/ingestion). Promote AI‑native ways of working across product, data, and engineering. Manage and mentor your engineering team, fostering a collaborative, high‑performance environment with excellent productivity. Demonstrate hands‑on technical excellence while modelling accountability, critical thinking, and AI‑first practices. Set clear objectives, provide regular feedback, conduct performance reviews.
• Delivery & accountability:
  Take full accountability for the delivery of high‑quality software products, owning both successes and challenges. Drive outcome‑focused delivery planning and execution, measuring success by business impact rather than effort or hours invested. Proactively identify and mitigate risks, making critical decisions to keep projects on track.
• Technical direction & architecture:
  Guide technical direction, establish best practices, and ensure architectural decisions support scalability, maintainability, and business goals. Be a hands‑on contributor for architecting and building well‑tested, maintainable applications. Optimize tech stacks and development workflows to maximize team productivity.
• Agile & product collaboration:
  Ensure Agile ceremonies (sprint planning, retrospectives, stand‑ups) are followed and continuously refine Agile practices to optimize team performance. Collaborate with product owners and stakeholders to develop and maintain technical roadmaps that align with business strategy. Work with product owners to build and maintain a prioritised backlog that balances business value, technical debt, and innovation.
• Quality & continuous improvement:
  Enforce rigorous quality standards including test‑driven development (TDD), code reviews, and automated testing. Ensure that security best…