Data Engineer

Data Engineer - Contract Position - HIRING ASAP Location: Remote Working Start Date: ASAP Duration: 12-Month Contract Daily Rate: £450 - £600 per day outside IR35 Summary Our client is building a Law Firm Digital Twin: a working simulation of how a law firm operates, built from the firm’s own data. Data from the firm’s operational systems (practice management and finance, HR, client onboarding, time recording) is mined into event logs; a discrete event simulation is built and calibrated from those logs; and the firm’s financial statements are reconstructed on top, so the effect of an operational change can be quantified before it is made. The stack spans three layers: data pipelines and a cloud warehouse (Snowflake), a simulation and statistical modelling core in Python, and a TypeScript product (Next.js, React, Postgres). This role owns the data layer: the pipelines that turn raw system data into the event logs and modelling-ready structures everything else runs on. It is a

hands-on building role in a small engineering team. Key ResponsibilitiesDesign, build and run the data platform: the pipelines and warehouse that turn raw data from client systems into data the models and the product can use.Own data quality end to end, from source extraction through to the datasets the modelling team relies on.Make new data sources usable: understand what they hold, extract from them, and shape the results into the platform’s structures.Work with the data scientist on what the models need, and with the software engineer on serving data to the product.Operate the infrastructure behind the data platform and keep it reliable, secure and affordable.Shape the data architecture as the platform scales.Key Skills & ExperienceAbove all, someone who has done analogous work: you have personally built the path from an enterprise’s operational systems into a warehouse and into event-shaped analytical data. Specifically:A hands-on pipeline builder. You have built and operated production pipelines recently and can walk through one in depth: the source quirks, the failure modes, the validation, the re-runs.Warehouse and SQL depth. SQL as a first language, and real experience with Snowflake or an equivalent cloud warehouse: modelling, performance, cost, access control.Comfortable with messy enterprise extracts. Old operational databases, inconsistent fields, batch-stamped timestamps, undocumented conventions. You profile before you assume, and your validation catches what documentation misses.Careful with sensitive data. The pipelines carry confidential HR and financial data; access control and auditability are part of the engineering, not overhead.Python and/or TypeScript for pipeline code, with version control, tests and review as habits. AI-assisted engineering as a habit (Claude Code or similar), with judgement about where not to trust it.Nice to haveExperience of law firm or professional services systems (Aderant, Elite, Intapp, SuccessFactors, Workday)Process mining and event-log analytics (e.g., QPR, Celonis, Signavio)Process simulation.