At a Glance
- Tasks: Own customer data engineering projects from discovery to delivery, building pipelines and models.
- Company: Fast-growing battery intelligence and electrification tech business at the forefront of data and AI.
- Benefits: Competitive salary, benefits, equity, and the chance to shape a growing engineering platform.
- Other info: Join early in the data engineering model's development and influence its future.
- Why this job: Make a real impact on complex data problems in a dynamic, innovative environment.
- Qualifications: Strong hands-on data engineering experience with AWS and Snowflake; leadership skills preferred.
The predicted salary is between 63000 - 77000 £ per year.
TECHNE is supporting a fast-growing battery intelligence and electrification technology business that is expanding its data engineering capability.
The company sits at the intersection of data, AI, and complex engineering, supporting applications across automotive, heavy industry, and energy storage. They are now hiring two Technical Specialist Data Engineers to help shape how customer data is engineered, delivered, and scaled.
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.
- Strong hands-on data engineering experience.
- Commercial experience with AWS and Snowflake.
- Experience building scalable data platforms, pipelines, transformations, and models.
- Strong understanding of data quality, validation, and reliability.
- 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.
- Join while the data engineering model is still being defined.
- Work with complex real-world data across AI, electrification, and engineering products.
- Join at a stage where your decisions will influence how the data capability develops as the business scales.
If you are an experienced Data Engineer looking for more ownership, broader technical exposure, and the chance to shape a growing engineering platform, please get in touch for a confidential discussion.
Senior Engineer, Data Engineering employer: TECHNE
TECHNE is an exceptional employer, offering a dynamic work environment where innovation meets real-world application in the defence technology sector. With a strong focus on employee growth and development, team members are encouraged to take ownership of their projects while collaborating closely with cutting-edge hardware. Located in a rapidly evolving industry, employees benefit from competitive compensation, a supportive culture, and the opportunity to contribute to groundbreaking propulsion systems that shape the future of aerospace and autonomous platforms.
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We think this is how you could land Senior Engineer, Data Engineering
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We think you need these skills to ace Senior Engineer, Data Engineering
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Craft a Tailored Cover Letter:For a full-time role at TECHNE, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at TECHNE. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
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✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at TECHNE!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
