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- Tasks: Design and optimise data pipelines for engineering and operational data.
- Company: Join a leading firm in the engineering and manufacturing sector.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborate with cross-functional teams and enhance your skills in a regulated industry.
- Why this job: Make an impact by working with cutting-edge data technologies in a dynamic environment.
- Qualifications: Experience in data engineering and proficiency with tools like Databricks and Spark.
The predicted salary is between 50000 - 65000 £ per year.
Proven experience as a Data Engineer working in engineering, manufacturing, or enterprise environments. Strong hands-on experience with ETL/ELT development across batch and streaming workloads. Skilled in building and maintaining data-streaming pipelines (e.g., Kafka, Spark Streaming, or equivalent). Proficiency with Databricks and Apache Spark for large-scale data processing. Experience with Hadoop and/or wider Apache ecosystem tools (e.g., Kafka, NiFi, Airflow). Strong understanding of Product Data Management (PDM), PLM, and/or engineering data structures. Experience integrating data from PDM, PLM, ERP, manufacturing, or other engineering systems. Hands-on experience with cloud data platforms (Azure Data Lake, Data Factory, Synapse, or similar). Ability to work with structured and unstructured engineering data across product lifecycle processes. Comfortable working in secure, regulated, and multi-national environments.
Key Responsibilities:
- Design, build, and optimise data ingestion, transformation, and integration pipelines.
- Implement scalable processing frameworks using Databricks, Spark, Hadoop, and Apache technologies.
- Develop and support real-time and near-real-time data-streaming pipelines for engineering and operational data.
- Integrate engineering datasets across PDM, PLM, ERP, manufacturing and digital engineering platforms.
- Support modeling and structuring of engineering data in alignment with architectural guidance.
- Assist Data Architects with the implementation of data standards and data models (light ISO 10303 exposure where required).
- Develop data solutions aligned with defined architectural frameworks and security requirements.
- Ensure data quality, lineage, metadata, and governance practices are implemented in all pipelines.
- Collaborate with architects, engineering SMEs, and cross-functional teams to gather requirements and deliver robust data solutions.
- Produce documentation, operational handover materials, and best-practice guidance.
- Troubleshoot, optimise, and maintain performance of cloud-based and distributed data systems.
- Experience with PLM platforms such as Siemens Teamcenter, Windchill, 3DX, or Aras.
- Exposure to ERP systems (SAP, Oracle, IFS, or equivalent).
- Familiarity with manufacturing systems (MES/MOM).
- Working knowledge of engineering data standards (e.g., ISO 10303 / STEP).
- Experience with data cataloguing, metadata management, lineage tools, or governance platforms.
- Understanding of engineering lifecycle processes, digital thread concepts, and systems engineering methodologies.
- Experience in large-scale defence, aerospace, or highly regulated engineering environments.
- Strong communication and stakeholder engagement skills.
Data Engineer employer: Stable
Contact Detail:
Stable Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer
✨Network Like a Pro
Get out there and connect with folks in the industry! Attend meetups, webinars, or even online forums related to data engineering. You never know who might have a lead on your dream job or can give you insider tips.
✨Show Off Your Skills
Create a portfolio showcasing your projects, especially those involving ETL/ELT processes or data streaming pipelines. Having tangible examples of your work can really set you apart when chatting with potential employers.
✨Ace the Interview
Prepare for technical interviews by brushing up on your knowledge of tools like Databricks, Spark, and Hadoop. Practice explaining your past projects and how you tackled challenges, as this will demonstrate your hands-on experience.
✨Apply Through Us!
Don’t forget to check out our website for open positions! Applying directly through us not only shows your interest but also gives you a better chance of landing that interview. We’re here to help you succeed!
We think you need these skills to ace Data Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience as a Data Engineer, especially in engineering and manufacturing environments. Use keywords from the job description to show we’re on the same page!
Showcase Your Skills: Don’t hold back on showcasing your hands-on experience with ETL/ELT development and data-streaming pipelines. Mention specific tools like Kafka, Spark, and Databricks to catch our eye!
Be Clear and Concise: When writing your application, keep it clear and to the point. We love straightforward communication, so make sure your skills and experiences shine without unnecessary fluff.
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of your application and ensures you don’t miss any important updates!
How to prepare for a job interview at Stable
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, like Databricks, Spark, and Kafka. Brush up on your ETL/ELT processes and be ready to discuss how you've implemented these in past projects.
✨Showcase Your Problem-Solving Skills
Prepare to share specific examples of challenges you've faced in data engineering roles. Highlight how you optimised data pipelines or resolved issues with data quality and governance. This will demonstrate your hands-on experience and critical thinking.
✨Understand the Business Context
Familiarise yourself with the company’s industry and how data engineering plays a role in their operations. Knowing how PDM, PLM, and ERP systems integrate can give you an edge in discussions about aligning data solutions with business needs.
✨Communicate Clearly and Confidently
Strong communication is key, especially when collaborating with cross-functional teams. Practice explaining complex technical concepts in simple terms, as this will show your ability to engage with stakeholders effectively.