At a Glance
- Tasks: Lead technical architecture and design data pipelines for AI/ML projects.
- Company: Innovative tech firm focused on cutting-edge data solutions.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic environment with opportunities to work on advanced technologies.
- Why this job: Join a team shaping the future of data engineering with impactful projects.
- Qualifications: 7+ years in data engineering on AWS with strong technical skills.
The predicted salary is between 80000 - 100000 £ per year.
Role summary: The overall technical lead and architect. Designs the metadata schema, builds the simulation onboarding pipeline, deploys metadata embedding pipeline and OpenSearch k-NN vector store, and authors data export format spec for AI/ML use case. This role is the deepest technical seat on the engagement:
- Key responsibilities
- Run the Sprint 1 architecture review of the existing UAT codebase (S3 + Glue + S3 Tables + OpenSearch + Athena) and deliver written gap findings.
- Design the metadata schema, taxonomy, and field catalogue (Light, Brain, Power).
- Tune data orchestration — Glue jobs, Athena queries, S3 Tables config, scheduling.
- Lead the deep-dive technical sessions with analysts on visualization requirements.
- Build and validate the simulation data onboarding pipeline against real data — including the 30 GB-per-run acoustic spectra dataset.
- Configure and validate the OpenSearch k-NN vector store and the Bedrock embedding pipeline.
- Author the AI/ML data export format specification and the AI onboarding pattern document.
- Co-design the API middleware blueprint with the Cloud Infrastructure Architect.
Must-have
- Principal-level hands-on data engineering on AWS — 7+ years.
- Deep production experience with S3, S3 Tables, Glue, Athena, and OpenSearch (including k-NN / vector search).
- Built and shipped vector embedding workloads.
- Strong metadata modelling and data taxonomy design experience for scientific or engineering domains.
- Comfort working with Parquet, JSON-LD, and large binary scientific data formats (mesh, time-series, spectra).
- Python proficiency; PySpark / Glue job tuning experience.
Nice-to-have / differentiators
- Prior simulation / CAE / HPC data lake experience (Ansys, Siemens NX, BETA CAE, OpenFOAM, etc.).
- Familiarity with surrogate model training data pipelines.
- Experience with SageMaker Unified Studio or comparable governed data-mesh tooling (in case of required integration).
- Multi-cloud data engineering (AWS GCP) experience.
- Published or contributed to AWS data architecture patterns or blueprints.
Data Engineer in Manchester employer: Response Informatics
As a leading employer in the tech industry, we offer Data Engineers an exceptional opportunity to work at the forefront of data architecture and engineering. Our collaborative work culture fosters innovation and creativity, while our commitment to employee growth ensures that you will have access to continuous learning and development opportunities. Located in a vibrant tech hub, we provide a dynamic environment where your contributions directly impact cutting-edge AI/ML projects, making your work both meaningful and rewarding.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer in Manchester
✨Tip Number 1
Network like a pro! Reach out to your connections in the data engineering field and let them know you're on the hunt for a new role. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving AWS, S3, Glue, and OpenSearch. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and data orchestration skills. Practice common data engineering problems and be ready to discuss your past experiences with metadata modelling and data taxonomy design.
✨Tip Number 4
Don't forget to apply through our website! We love seeing applications come directly from candidates who are excited about joining our team. Plus, it gives us a chance to see your enthusiasm right from the start!
We think you need these skills to ace Data Engineer in Manchester
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Data Engineer role. Highlight your hands-on experience with AWS, S3, Glue, and OpenSearch. We want to see how your skills match up with our needs!
Showcase Your Projects:Include specific projects where you've designed metadata schemas or built data pipelines. We love seeing real examples of your work, so don’t hold back on the details!
Be Clear and Concise:When writing your application, keep it clear and to the point. Use bullet points for key achievements and responsibilities. We appreciate straightforward communication!
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. We can’t wait to hear from you!
How to prepare for a job interview at Response Informatics
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technologies mentioned in the job description, especially AWS services like S3, Glue, and OpenSearch. Brush up on your Python skills and be ready to discuss your hands-on experience with vector embedding workloads.
✨Prepare for Technical Deep Dives
Expect to dive deep into technical discussions during the interview. Prepare to explain your past projects, particularly those involving metadata schema design and data orchestration. Be ready to showcase how you’ve tackled challenges in previous roles.
✨Showcase Your Problem-Solving Skills
Think of specific examples where you identified gaps in existing systems or processes. Be prepared to discuss how you approached these issues, what solutions you implemented, and the impact they had on the project or team.
✨Familiarise Yourself with the Company’s Domain
Research the company’s focus areas, especially if they relate to scientific or engineering domains. Understanding their specific needs will help you tailor your responses and demonstrate that you’re not just a fit for the role, but also for the company culture.
