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
- 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
- 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
- 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
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Senior Data Engineer employer: Trust In SODA
As a leading employer in the tech industry, we offer a dynamic work environment that fosters innovation and collaboration. Our London office provides a hybrid working model, allowing for flexibility while being part of a vibrant team dedicated to delivering impactful solutions. We prioritise employee growth through continuous learning opportunities and support, making this an ideal place for professionals looking to advance their careers in solution architecture.
