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- Tasks: Design and optimise data platforms using Snowflake and AWS for impactful projects.
- Company: Join a leading tech firm focused on innovation in data architecture.
- Benefits: Attractive salary, flexible working options, and opportunities for skill development.
- Why this job: Be at the forefront of data engineering and AI, shaping the future of analytics.
- Qualifications: Expertise in Snowflake, SQL, and data engineering principles required.
- Other info: Dynamic team environment with potential for rapid career advancement.
The predicted salary is between 60000 - 80000 £ per year.
Required Technical Skills
- Data Platform
- Deep knowledge of Snowflake architecture, performance tuning, data sharing, security, and workload optimization.
- Cloud Platform
- Strong experience with Amazon Web Services, including:
- S3
- IAM
- AWS Glue
- Lambda
- CloudWatch
- Data Transformation
- Strong expertise in SQL for data transformation and performance optimization.
- Python (preferred) for automation and data engineering tasks.
- Enterprise ETL / ELT pipeline architecture.
- Data warehousing and enterprise data modelling.
- Dimensional modelling (Star Schema, Snowflake Schema).
- Data pipeline scalability and reliability design.
- Experience supporting AI/ML data pipelines and data preparation for machine learning models.
- Understanding of predictive analytics, recommendation engines, and customer behaviour analytics.
- Ability to design AI-ready data platforms for future analytics use cases.
- Experience with Apache Airflow for pipeline orchestration.
- Knowledge of CI/CD pipelines, DevOps, and Git-based development workflows.
- Experience with data governance, metadata management, and enterprise data catalog tools.
- Experience with BI tools such as:
- Tableau
- Microsoft Power BI
Snowflake Data Architect employer: Test Yantra
Contact Detail:
Test Yantra Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Snowflake Data Architect
✨Tip Number 1
Network like a pro! Reach out to folks in your industry, especially those who work with Snowflake or AWS. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects related to data engineering and AI/ML. This is your chance to demonstrate your expertise in SQL, Python, and DBT.
✨Tip Number 3
Prepare for interviews by brushing up on common questions related to Snowflake architecture and ETL processes. We recommend practising with a friend or using mock interview platforms to build confidence.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Snowflake Data Architect
Some tips for your application 🫡
Show Off Your Snowflake Skills: Make sure to highlight your deep knowledge of Snowflake architecture and performance tuning in your application. We want to see how you can optimise workloads and ensure data security, so don’t hold back on those details!
Cloud Experience is Key: Since we’re all about AWS, be sure to mention your experience with services like S3, IAM, and Lambda. If you've worked with AWS Glue or CloudWatch, let us know! This will show us you’re ready to hit the ground running.
SQL and Python Proficiency: We love a good SQL query! Make sure to showcase your expertise in SQL for data transformation and any Python skills you have for automation. This is your chance to demonstrate how you can optimise performance and streamline processes.
Tailor Your Application: Take a moment to tailor your application to our job description. Mention any relevant experience with ETL/ELT pipelines, data modelling, or AI/ML data preparation. We appreciate when candidates take the time to connect their skills to what we’re looking for!
How to prepare for a job interview at Test Yantra
✨Know Your Snowflake Inside Out
Make sure you have a deep understanding of Snowflake architecture and its features. Be ready to discuss performance tuning, data sharing, and workload optimisation. Brush up on real-world examples where you've successfully implemented these skills.
✨Show Off Your Cloud Skills
Familiarise yourself with AWS services like S3, IAM, and Lambda. Prepare to explain how you've used these tools in past projects, especially in relation to data transformation and pipeline management. Highlight any specific challenges you faced and how you overcame them.
✨SQL and Python Proficiency
Be prepared to demonstrate your SQL expertise, particularly in data transformation and performance optimisation. If you have experience with Python for automation, share specific examples of how you've used it in data engineering tasks. Consider preparing a small coding challenge to showcase your skills.
✨Understand the Bigger Picture
Discuss your experience with AI/ML data pipelines and how they relate to predictive analytics. If you have knowledge of BI tools like Tableau or Power BI, mention how you've used them to derive insights from data. This shows you're not just a techie but also understand the business impact of your work.