At a Glance
- Tasks: Design and build AI features for real-time business planning workflows.
- Company: Join a leading tech firm at the forefront of AI innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team environment with excellent career advancement potential.
- Why this job: Make a significant impact by developing cutting-edge AI solutions.
- Qualifications: Strong background in data engineering and machine learning expertise required.
The predicted salary is between 80000 - 100000 £ per year.
About the Role
We’re looking for a versatile Principal Data Engineer who can work across the full stack of Anaplan AI applications – from model integration to setting the technical direction for ingesting, transforming, storing, serving, and governing the data that powers our LLM‑based and agentic systems. The role builds AI features that can be used in real‑time, enabling business users to leverage GenAI in their planning workflows. Candidates must have deep knowledge of machine learning and strong data engineering skills.
Your Impact
- Design and build the retrieval layer powering RAG and agentic workloads, including vector and graph databases, hybrid search, and knowledge‑graph architecture.
- Develop end‑to‑end GenAI features, including backend API services, model integration, monitoring, evaluation, and deployments.
- Engineer feature and context pipelines that balance batch and streaming patterns to feed forecasting and anomaly‑detection models, collaborating closely with data scientists to productionize algorithms.
- Build a data plane for evaluation, implementing rigorous frameworks to continuously monitor, measure, and improve GenAI feature quality, accuracy, latency, and user satisfaction.
- Collaborate with data scientists to productionize ML models and forecasting algorithms.
Your Skills
- Extensive background in Data Science Engineering with a clear track record of principal‑level technical leadership.
- Hands‑on experience building and shipping AI/ML products in production.
- Deep practical experience with LLM‑based systems: RAG architectures, embedding pipelines, prompt and response logging, evaluation frameworks.
- Hands‑on expertise with vector databases, graph databases, and knowledge graphs.
- End‑to‑end exposure to model lifecycle development, including training and deploying ML models in production environments.
- Deep knowledge of LLM APIs, prompt engineering, and conversational AI patterns.
- Proficiency in Python and modern software development practices (testing, code review, CI/CD).
Preferred Skills
- Hands‑on experience with cloud‑native ML infrastructure platforms.
- Knowledge of vector databases (Pinecone, Weaviate, Qdrant) and embedding models.
- Experience with model serving frameworks (vLLM, TensorRT, Ray).
- Familiarity with Anaplan or similar enterprise planning platforms.
- Experience with A/B testing and experimentation frameworks for AI features.
- Experience with model observability tools.
Principal Data Engineer employer: Anaplan
As a Principal Data Engineer at our innovative company, you will thrive in a dynamic work culture that champions collaboration and creativity. We offer competitive benefits, including professional development opportunities and a supportive environment that encourages continuous learning and growth. Located in a vibrant tech hub, our team is dedicated to pushing the boundaries of AI technology while ensuring a healthy work-life balance for all employees.
StudySmarter Expert Advice🤫
We think this is how you could land Principal Data Engineer
✨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 Principal Data Engineer 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 past projects, especially those involving AI/ML products. This will give potential employers a taste of what you can bring to the table and how you can contribute to their team.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Be ready to discuss your experience with LLM-based systems and data pipelines, as well as any challenges you've faced and how you overcame them.
✨Tip Number 4
Don't forget to apply through our website! We make it easy for you to find and apply for roles that match your skills. Plus, it shows us you're genuinely interested in joining our team!
We think you need these skills to ace Principal Data Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the Principal Data Engineer role. Highlight your hands-on experience with AI/ML products and any relevant projects you've worked on. We want to see how you can bring value to our team!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about data engineering and how your background aligns with our mission at StudySmarter. Don’t forget to mention specific technologies or methodologies you’ve used that relate to the job description.
Showcase Your Projects:If you've worked on any relevant projects, whether in a professional setting or as personal endeavours, make sure to include them. We love seeing practical applications of your skills, especially those involving LLM-based systems or data pipelines!
Apply Through Our Website:We encourage you to apply directly through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to submit all your materials in one go. Plus, we love seeing candidates who take the initiative to connect with us directly!
How to prepare for a job interview at Anaplan
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technologies mentioned in the job description, especially around LLM-based systems and data engineering. Brush up on your knowledge of vector databases and graph architectures, as these will likely come up during technical discussions.
✨Showcase Your Projects
Prepare to discuss specific projects where you've built or shipped AI/ML products. Highlight your role in the end-to-end process, from model integration to deployment, and be ready to explain the challenges you faced and how you overcame them.
✨Collaborate Like a Pro
Since collaboration with data scientists is key, think of examples where you’ve successfully worked in cross-functional teams. Be prepared to discuss how you’ve contributed to productionising algorithms and improving feature quality through teamwork.
✨Ask Insightful Questions
Prepare thoughtful questions about the company’s approach to GenAI and their data strategy. This shows your genuine interest in the role and helps you gauge if the company aligns with your career goals and values.
