[Hiring] Forward Deployed Data Solution Engineer @Accelerant

Role Description

This is not a traditional enterprise architecture role. Accelerant is operating in a period of rapid change. Significant internal systems across finance, operations, and data are being implemented. Business users across the organisation are building AI-enabled capabilities at a pace that exceeds the capacity of central engineering teams to review and govern them. The ways in which software is designed and built are themselves changing, with AI-assisted development and agentic tooling becoming part of everyday practice.

The Forward-Deployed Solution Engineer exists to respond to all of this in real time, from inside the teams doing the work, not from a distance. This person is embedded with delivery programmes and product teams. They review decisions and code as they are made, not after the fact. They drive solutions quickly, make independent technical judgements, and hold the quality bar without becoming a bottleneck. They are the link between what the business needs to build and what can actually be operated and maintained at enterprise scale.

What You Will Do

• Be present in the work: Sit inside delivery teams for the duration of engagements, attend design and development sessions, review technical decisions as they are being made, and intervene when something is heading in the wrong direction.
• Validate and guide continuously: Continuously assess whether what is being built conforms to Accelerant's architecture principles, data standards, and integration patterns. Propose better paths and create documented patterns where they do not exist.
• Take decisions quickly: Make sound technical decisions with available information and move forward without waiting for consensus where it is not needed.
• Bridge business-built capability to production: Assess capabilities built using AI tools, low-code platforms, and scripting environments, identify gaps, and create a clear path to production.
• Own integration design: Design and validate integration patterns between enterprise systems and ensure integrations are clean, well-documented, and built to a standard.
• Drive data product quality and governance: Work with delivery teams and the Data Office to ensure data products are described at source, ownership is assigned, and canonical data models are accurate and accessible.
• Make knowledge durable: Ensure technical decisions are recorded, architectural decision records exist, and knowledge required to support what Accelerant builds is documented.

What You Will Not Do

• Write lengthy architecture documents that no one reads.
• Operate as a governance gate that slows delivery without adding value.
• Wait for others to bring problems to you.
• Review things in isolation from the teams building them.
• Produce opinions without evidence.

Skills and Experience

• Technical foundation: Strong practical knowledge of cloud platforms, particularly Azure and AWS. Comfortable with API design and integration patterns, including REST and event-driven architectures.
• AI and modern development practice: Understanding of AI-assisted development approaches and practical experience with at least one AI development framework.
• Domain awareness: Experience working with enterprise SaaS platforms, particularly in finance, operations, or insurance domains.
• Communication and delivery: Ability to communicate clearly with both engineers and business stakeholders, adjusting register accordingly.
• Ways of working: Comfortable with ambiguity, able to engage with problems that are not fully defined and document thinking.

What Good Looks Like

• Delivery programmes you have been embedded with are running with fewer design-level surprises after build starts.
• Integration patterns you have reviewed or created are being used by other teams as a starting point.
• Business-built capabilities that have come through your assessment have a clear production status, documented ownership, and a supportability record.
• Technical decisions made during your engagements are findable on Confluence with enough context for newcomers.
• The teams you have worked with ask for you to be involved earlier in their next piece of work, not later.