At a Glance
- Tasks: Lead technical architecture and design innovative data solutions for AI/ML projects.
- Company: Join a forward-thinking tech company at the forefront of data engineering.
- Benefits: Attractive salary, flexible working options, and opportunities for professional growth.
- Other info: Dynamic team environment with opportunities to work on exciting projects.
- Why this job: Make a significant impact in data engineering while working with cutting-edge technologies.
- Qualifications: 7+ years of hands-on AWS data engineering experience and strong Python skills.
The predicted salary is between 80000 - 100000 £ per year.
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 on this engagement:
- 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 employer: Zensar Technologies
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
✨Tip Number 1
Network like a pro! Reach out to your connections in the data engineering field, especially those who work with AWS. A friendly chat can lead to insider info about job openings that aren't even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving 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 taxonomy design.
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who are genuinely interested in joining our team. Plus, it makes it easier for us to keep track of your application and get back to you quickly.
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 with AWS and the specific tools mentioned in the job description. We want to see how your skills align with our needs, so don’t be shy about showcasing your hands-on data engineering experience!
Craft a Compelling Cover Letter:Your cover letter is your chance to tell us why you’re the perfect fit for this role. Share your passion for data engineering and how your past projects relate to the responsibilities listed. We love hearing about your journey!
Showcase Your Technical Skills:In your application, make sure to highlight your proficiency in Python, PySpark, and any relevant data formats like Parquet or JSON-LD. We’re looking for someone who can hit the ground running, so let us know what you’ve built and shipped!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy – just follow the prompts and submit your materials!
How to prepare for a job interview at Zensar Technologies
✨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 approach to designing metadata schemas and data orchestration. Have examples ready that showcase your problem-solving skills and how you've tackled similar challenges in past projects.
✨Showcase Your Project Experience
Be ready to talk about specific projects where you’ve built or shipped data pipelines, especially those involving large datasets. Highlight your experience with simulation data onboarding and any relevant tools you’ve used, like PySpark or Athena.
✨Ask Insightful Questions
Interviews are a two-way street! Prepare thoughtful questions about the team’s current architecture and any challenges they face. This shows your genuine interest in the role and helps you assess if it’s the right fit for you.
