Data Architect

Position Summary

The Data Architect owns the technical foundation of a new data-intensive benchmarking and analytics capability embedded in Onit's enterprise legal operations platform. This is a hands-on, engineering-focused architecture role at the start of a multi-phase programme, and the work is front-loaded: within the first quarter, the Data Architect will select the data platform, design the canonical data model that several product surfaces depend on, define the service contracts, and set the performance and multi-tenant data-isolation guarantees the team builds against. Working closely with the Lead Engineer, Lead Designer, Product Management, and Data Engineering, this role stays in the code, the query plans, and the review conversations rather than handing designs over a wall.

• Architecture Ownership: Own the end-to-end architecture for a data-intensive analytics capability embedded in an existing enterprise product.

• Data Platform Selection: Evaluate and select the data platform for large-scale analytical queries, with a documented cost and performance basis for the decision.

• Canonical Data Model: Design a canonical data model that multiple source systems feed and multiple product surfaces consume, ensuring figures reconcile consistently across every surface.

• Service Contracts & APIs: Define and publish service contracts and API specifications using a contract-first approach.

• Non-Functional Requirements: Set and defend latency targets, refresh cadence, and availability requirements, and make them measurable rather than aspirational.

• Multi-Tenant Data Isolation: Design tenant data isolation so that it is enforced structurally, not by convention.

• Architecture Decisions & Reviews: Write architecture decision records that explain trade-offs honestly, including what would change the decision, and review designs and code across the team.

• Cross-Functional Translation: Translate technical constraints into terms Product and Design can act on, and translate their requirements back into architecture.

• Typically 10+ years in software engineering, including substantial time in an architect, principal, or staff-level role owning system-level decisions.

• Demonstrable experience designing data-intensive applications: systems where query performance over large datasets is the central problem.

• Experience evaluating and selecting data platforms for large-scale analytical workloads, with a documented cost and performance basis for the decision.

• Strong SQL and relational data modelling; practical experience with analytical or columnar data stores and a clear view of when each is appropriate.

• Experience designing canonical data models that multiple source systems feed and multiple product surfaces consume, with figures that reconcile consistently.

• Experience designing and operating multi-tenant SaaS systems, including data isolation enforced structurally rather than by convention.

• Hands-on performance engineering: able to profile a slow query, decompose a latency budget, and define what to measure and at which percentile.

• Writes decisions down clearly (e.g., architecture decision records that explain trade-offs honestly) and can defend them to both engineers and non-engineers.

• Working proficiency with AI tools (e.g., Claude, GitHub Copilot) to accelerate design exploration, query analysis, and documentation, with the judgment to critically evaluate AI-generated output.

• API design experience with a contract-first mindset.

• Cloud platform experience (AWS), including infrastructure as code.

• Experience retrofitting analytics or reporting capability into an existing transactional product.

• Experience working with incomplete or inconsistent source data, and designing systems that stay trustworthy despite it.

• Background in enterprise B2B SaaS or the legal technology domain.

• Experience with data anonymisation or aggregation where privacy guarantees matter.