Senior Data/ML Engineer – Contract Position – HIRING ASAPLocation: London Bridge – In office 2-3 days per week Start Date: ASAP Duration: 3-4 months with extension Daily Rate: £400 - £450 per day outside IR35SummaryYou will sit with the Head of Data and the Lead Data Scientist/Engineer. You will be pointed at inputs and expected outcomes, then expected to design and build the path between them - including the data model – with light review.The work still must be grounded: clear schemas, sensible storage layout, production-quality Python. It is not cowboy scripts, and it is not waiting for a backlog of tickets.This is a bad fit if you mainly plug enterprise components together, wait for JIRA epics, or treat AI coding tools as a novelty. This is a good fit if you have built data/ML systems in a startup or small product team, you use Cursor/Copilot (or equivalent) as a normal part of shipping, and you can own a problem from messy source files to a running pipeline without being sequencedRequirementsStrong production PythonEvidence of designing data models and schemas, not only consuming themComfort operating with incomplete requirements: inputs and outcomes, then you fill in the middleCan take messy inputs and an expected outcome, then design schema + build the pipeline with light reviewEvidence of designing a production pipeline from messy source data, not just orchestrator configAI-assisted development as a default way of working, not a talking pointUsing AI coding tools (Cursor, Copilot or equivalent as a normal way of shipping4+ years shipping data or applied ML systems in productionPrevious experience in a start-up or a small product teamBonus SkillsDagster, or Airflow, or Prefect in productionData lakes / Parquet / S3Terraform or general cloud familiarity (infra is owned by another team)RAG, embeddings, or other LLM-adjacent pipelinesStartup or small-team product deliveryResponsibilitiesTurn client data (APIs, CSVs, S3, messy operational exports) into reliable Python pipelines.Specify schemas and storage layout (Parquet on S3, layered / medallion-style) so the next person can extend the work.Orchestrate jobs in Python. We use Dagster; Airflow, Prefect, or well-structured Python jobs are fine.Work on AWS. You do not need to own Terraform, EKS, or networking.Use AI coding agents heavily, then stand behind the architecture and the data model.Shape approach with the rest of the data team: enough design to stay coherent, then execute at speedSkills we’re not looking forAssembling warehouse / lakehouse platforms (Spark, Informatica, “I wired Airflow to the lake”)Writing TDDs and JIRA epics rather than shipping codeLarge bank / SI / programme delivery with little product ownershipML research / model-training CVs with no real-world data engineering
Nathan Peters