> Markdown version of [/jobs/ext/4035201-data-engineer-backend](https://www.wearedevelopers.com/jobs/ext/4035201-data-engineer-backend). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data Engineer (Backend)

- **Company:** Ocean Infinity
- **Location:** London, UK
- **Experience:** Experienced
- **Salary:** £55,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Databases, Continuous Integration, Data Structures, Python (Programming Language), PostgreSQL, Machine Learning, MongoDB, Redis, SQL Databases, Data Streaming, Unstructured Data, Workflow Management Systems, Delivery Pipeline, Backend, Data Lakes, Kubernetes, Information Technology, Data Management, Data Pipelines
- **Published:** October 7, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=27b7aaf068521dd6

## About the Role

* A degree in Computer Science, Mathematics, or a related field, plus 3+ years as a Data Engineer, Backend Engineer, or similar.
 * Strong in Python, comfortable with SQL, and a solid grasp of data structures and algorithms.
 * Experience building pipelines, APIs, or backend services connected to data platforms.
 * Comfortable working with data lakes and both structured and unstructured data.
 * Hands-on experience with orchestration tools like Airflow, Prefect, or Flyte.
 * Familiar with databases such as Postgres, MongoDB, or Redis.
 * A clear communicator who works well across different teams.

Nice to have!

 * Prior experience with ocean data or robotics/IoT/edge device data
 * Experience with Delta Lake or similar platforms, and with streaming data.
 * Experience with CI/CD and containerized services (e.g., Kubernetes).
 * Some experience with ML model serving and observation

## Description

* Work closely with product teams, AI engineers, subject-matter experts, and data platform engineers to understand goals and design appropriate solutions.
 * Build pipelines and integrations that move data between our lakehouse, AI systems, and the products we ship.
 * Build the datasets for AI engineers to train, test, and run models on.
 * Build APIs and backend services so other teams can access datasets and model outputs.
 * Help AI engineers quickly build and test small model services and prototypes.
 * Watch over data quality, identify problems early, fix them, and improve performance where you can.
 * Write clear documentation so other teams can understand and use what you've built.