At a Glance
- Tasks: Design and implement data integration solutions using Snowflake and dbt.
- Company: Join a leading tech firm focused on innovative data solutions.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on continuous learning.
- Why this job: Be at the forefront of data technology and make a real impact.
- Qualifications: 7 years in data engineering with strong SQL and dbt skills.
The predicted salary is between 60000 - 80000 € per year.
We are seeking a talented and experienced Data Warehouse Modeler and Engineer to join the Data Solutions domain in EMEA. As a key contributor, you will be responsible for designing and implementing end-to-end data integration and transformation solutions on Snowflake. This role combines ETL/ELT design, data mapping, data modeling, and hands-on data engineering using dbt.
Key Responsibilities:
- Design and implement ETL/ELT pipelines for data ingestion, transformation, and consolidation.
- Perform data mapping from source systems to target Snowflake structures.
- Design and maintain conceptual, logical, and physical data models, staging, and integration marts.
- Develop, test, and maintain dbt models following best practices.
- Optimize Snowflake performance, query design, materializations, and clustering.
- Ensure data quality, consistency, lineage, and documentation.
- Collaborate with engineering, analytics, and platform teams to align on data patterns and standards.
Technical Qualifications:
- Bachelor's or Master's degree in Computer Science, Data Engineering, or a related technical field.
- 7 years of experience in a similar role within a fast-paced, enterprise-scale environment.
- Strong proficiency in SQL and hands-on experience with DBT Core or Cloud or any other traditional ETL Tools (ODI, Informatica, Talend, SSIS, etc.).
- Snowflake data engineering, SQL data modeling, Kimball, Inmon, Data Vault concepts, ETL/ELT design, and data mapping.
- Data quality governance and performance optimization.
- A solid understanding of end-to-end data ingestion and transformation processes.
- AWS data stack (S3, Glue, EMR, PySpark, Iceberg - nice to have).
Non-Technical Skills:
- Demonstrated commitment, ownership, and accountability.
- Strong attention to detail with a proactive approach to identifying and resolving problems.
- A genuine interest in emerging data technologies and a curiosity about continuous improvements.
- Excellent verbal and written communication skills.
Data Warehouse Modeler in Essex employer: TESTQ Technologies
Join a forward-thinking company that values innovation and collaboration, where as a Data Warehouse Modeler, you will play a pivotal role in shaping data solutions within the EMEA region. Our inclusive work culture fosters continuous learning and professional growth, offering you the chance to work with cutting-edge technologies like Snowflake and DBT while enjoying a supportive environment that prioritises employee well-being and development.
StudySmarter Expert Advice🤫
We think this is how you could land Data Warehouse Modeler in Essex
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. 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 data models, ETL pipelines, and any projects you've worked on. 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 your SQL and Snowflake knowledge. Be ready to discuss your experience with DBT and how you've tackled data quality issues in the past. Practice makes perfect!
✨Tip Number 4
Don't forget to apply through our website! We love seeing applications come directly from candidates who are excited about joining us. Plus, it shows you're serious about landing that Data Warehouse Modeler role.
We think you need these skills to ace Data Warehouse Modeler in Essex
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Data Warehouse Modeler role. Highlight your experience with Snowflake, DBT, and any relevant ETL/ELT tools. We want to see how your skills match what we're looking for!
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 makes you a perfect fit for our team. Keep it engaging and personal – we love to see your personality!
Showcase Your Projects:If you've worked on any relevant projects, make sure to mention them in your application. Whether it's a personal project or something from your previous job, we want to see how you've applied your skills in real-world scenarios.
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!
How to prepare for a job interview at TESTQ Technologies
✨Know Your Snowflake
Make sure you brush up on your Snowflake knowledge before the interview. Understand its architecture, features, and how it integrates with ETL/ELT processes. Being able to discuss specific use cases or projects where you've implemented Snowflake will really impress the interviewers.
✨Showcase Your SQL Skills
Since strong proficiency in SQL is a must-have, prepare to demonstrate your skills. You might be asked to solve a problem on the spot or explain your thought process behind a complex query. Practising common SQL scenarios can help you feel more confident during this part of the interview.
✨Talk About Data Modelling
Be ready to discuss your experience with data modelling techniques like Kimball, Inmon, and Data Vault. Prepare examples of how you've designed and maintained data models in previous roles, and be clear about the challenges you faced and how you overcame them.
✨Emphasise Collaboration
This role involves working closely with engineering, analytics, and platform teams. Highlight your teamwork experiences and how you've successfully collaborated on data projects. Sharing specific examples of how you aligned data patterns and standards with other teams will show that you're a great fit for their collaborative culture.
