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- Tasks: Lead the design of ML environments and optimise data pipelines using Snowflake.
- Company: Join a global lifestyle brand's innovative customer data science team.
- Benefits: Competitive salary up to £75k, hybrid work, and a transformative role.
- Why this job: Shape best practices in data engineering and make a real impact on customer insights.
- Qualifications: Strong Python skills, experience with Snowflake, and MLOps knowledge required.
- Other info: Exciting opportunity for career growth in a dynamic environment.
The predicted salary is between 60000 - 90000 £ per year.
Senior Data Engineer – (Data Science & Engineering)
Hybrid – UK | London | Up to £75k DOE | 12-Month FTC | J12995
This is an exciting opportunity to join a global lifestyle brand’s customer data science team during a transformative phase. As the business transitions to inhouse capabilities for CRM and customer insights, alongside implementing a new customer data platform, this role is key in shaping best practices and ensuring seamless collaboration with IT partners.
Seeking an experienced and motivated Senior Data Engineer with a strong background in MLOps and Snowflake to help scale the data infrastructure and support analytical and data science workflows. Your work will enable faster, more reliable access to customer data and insights that drive more relevant and personalised interactions across the business.
Key Responsibilities
- Lead the design and implementation of a production-ready ML environment in collaboration with IT and data science teams.
- Define best practices for model deployment, monitoring, and governance.
- Build and optimize data pipelines using Snowflake and Snowpark to support ML workflows.
- Support the migration of existing models into the new environment, identifying and resolving blockers.
- Champion MLOps principles across the team, mentoring others and fostering a culture of excellence.
- Ensure compliance with data governance, privacy, and security.
Required Skills
- Strong proficiency in Python, especially for data manipulation and transformation
- Hands-on experience with Snowflake, including Snowpark for advanced data engineering tasks
- Solid experience of SQL, data modelling, and modern data warehouse architecture
- Experience with data orchestration, workflow management, and CI/CD practices
- Experience in deploying and maintaining scalable data pipelines
- Experience of MLOps practices and working with data science teams
- Experience with tools like MLflow or other model tracking/versioning tools
- Experience of feature stores and data pipelines for ML/recommendation use cases
Let’s talk… APPLY NOW
No sponsorship or visa holder applications can be accepted.
#DataEngineering #Snowflake #Snowpark #MachineLearning #DatatechAnalytics
Senior Data Engineer - Snowflake & MLOps employer: Datatech Analytics
Contact Detail:
Datatech Analytics Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Engineer - Snowflake & MLOps
✨Tip Number 1
Network like a pro! Reach out to your connections in the data engineering field, especially those who work with Snowflake and MLOps. 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 Python, Snowflake, and MLOps. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common data engineering scenarios. Be ready to discuss how you've tackled challenges in building data pipelines or implementing ML environments. We want to see your problem-solving skills in action!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Senior Data Engineer - Snowflake & MLOps
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Senior Data Engineer role. Highlight your experience with Snowflake, MLOps, and any relevant projects that showcase your skills in data engineering and model deployment.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about this role and how your background aligns with the company's goals. Don’t forget to mention your experience with data pipelines and collaboration with IT teams.
Showcase Your Technical Skills: Be specific about your technical skills in Python, SQL, and data orchestration tools. Mention any hands-on experience you have with MLflow or similar tools, as these are key for the role. We want to see your expertise!
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at Datatech Analytics
✨Know Your Tech Inside Out
Make sure you brush up on your Python skills, especially for data manipulation and transformation. Familiarise yourself with Snowflake and Snowpark, as well as MLOps principles. Being able to discuss specific projects where you've used these technologies will really impress the interviewers.
✨Showcase Your Problem-Solving Skills
Prepare to discuss how you've tackled challenges in previous roles, particularly around model deployment and data pipeline optimisation. Think of examples where you identified blockers and how you resolved them, as this will demonstrate your proactive approach and technical expertise.
✨Understand the Business Context
Research the company and its customer data science team. Understand their goals during this transformative phase and think about how your role as a Senior Data Engineer can contribute to their success. This shows that you're not just technically skilled but also aligned with their vision.
✨Be Ready to Discuss Best Practices
Since you'll be defining best practices for model deployment and governance, come prepared with insights on what you consider best practices in MLOps. Share your thoughts on compliance with data governance and security, as this will highlight your understanding of the broader implications of your work.