Senior Data/ML Engineer – Contract Position – HIRING ASAP

Location: 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 IR35

Summary

You 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 sequenced

Requirements
  • Strong production Python
  • Evidence of designing data models and schemas, not only consuming them
  • Comfort operating with incomplete requirements: inputs and outcomes, then you fill in the middle
  • Can take messy inputs and an expected outcome, then design schema + build the pipeline with light review
  • Evidence of designing a production pipeline from messy source data, not just orchestrator config
  • AI-assisted development as a default way of working, not a talking point
  • Using AI coding tools (Cursor, Copilot or equivalent as a normal way of shipping
  • 4+ years shipping data or applied ML systems in production
  • Previous experience in a start-up or a small product team
Bonus Skills
  • Dagster, or Airflow, or Prefect in production
  • Data lakes / Parquet / S3
  • Terraform or general cloud familiarity (infra is owned by another team)
  • RAG, embeddings, or other LLM-adjacent pipelines
  • Startup or small-team product delivery
Responsibilities
  • Turn 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 speed
Skills we’re not looking for
  • Assembling warehouse / lakehouse platforms (Spark, Informatica, “I wired Airflow to the lake”)
  • Writing TDDs and JIRA epics rather than shipping code
  • Large bank / SI / programme delivery with little product ownership
  • ML research / model-training CVs with no real-world data engineering