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# Data Engineer

- **Company:** TECHNE
- **Location:** Oxford, UK
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Cloud Computing, Customer Data Management, Information Engineering, Machine Learning, System Availability, Snowflake, Data Management, Data Pipelines
- **Published:** September 15, 2026
- **Apply:** https://www.collegerecruiter.com/job/2879225123-data-engineer

## About the Role

* Strong hands-on data engineering experience.
 * Commercial experience with AWS and Snowflake.
 * Experience building scalable data platforms, pipelines, transformations, and models.
 * Comfortable working directly with customers and senior technical stakeholders.
 * Strong understanding of data quality, validation, and reliability.
 * Interested in taking broader technical ownership rather than staying within a narrow engineering remit.

Desirable Experience:

 * AI or machine learning data workflows.
 * Telemetry, IoT, or time-series data.
 * Technical or project leadership.
 * Experience within automotive, energy, industrial technology, or another engineering-led environment.
 * Battery experience is not required.

## Description

This is not a traditional Data Engineer position focused purely on pipelines and platform maintenance.

You will take ownership of customer-facing data engineering projects from technical discovery through to delivery, while helping define the standards, architecture, and delivery model used as the team grows.

You will stay hands-on technically, working across AWS, Snowflake, data pipelines, transformations, validation, and AI-ready workflows, while partnering with Data Science, AI, Product, Cloud, and specialist engineering teams., * Own customer data engineering projects from discovery through to delivery.

 * Build and develop pipelines, transformations, and data models across AWS and Snowflake.
 * Work directly with customers to understand complex data problems and define the technical approach.
 * Support data workflows feeding AI and machine learning applications.
 * Improve data quality, validation, observability, and platform reliability.
 * Help establish repeatable engineering standards and delivery practices.
 * Lead technical workstreams as the platform and customer base grow.


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