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- Tasks: Join us as a Data Engineer to build and optimize data architectures on Snowflake.
- Company: We are a dynamic team focused on delivering valuable data products to our clients.
- Benefits: Competitive salary of £700 Umbrella for 6 months, with opportunities for growth.
- Why this job: Be part of a small, innovative team where your contributions directly impact our data solutions.
- Qualifications: Experience with Data Vault 2.0, Snowflake, SQL, and strong programming skills in Python or Java required.
- Other info: Ideal for self-starters who thrive in collaborative environments and enjoy problem-solving.
The predicted salary is between 42000 - 84000 £ per year.
Job Title: Data Engineer
Salary: £700 Umbrella (6 Months)
We are looking for a forward thinking Data Engineer to build a robust Vault 2.0 architecture on Snowflake, to better serve and deliver valuable data products to our customers (internal and end-client).
Key Responsibilities:
1. Data Vault 2.0 Implementation:
o Design and implement Data Vault 2.0 architecture on the Snowflake data platform.
o Develop and maintain hubs, links, and satellites in the Data Vault 2.0 model.
2. Data Integration:
o Build robust and scalable data pipelines to ingest and process data from various sources (e.g., databases, APIs, streaming platforms).
o Implement ETL/ELT processes to ensure efficient data loading, transformation, and storage.
3. Data Marts:
o Design and build data marts to support business intelligence and analytical requirements.
o Collaborate with business stakeholders to understand data needs and deliver tailored data products.
4. Performance Optimization:
o Optimize data models, queries, and storage solutions for performance and cost efficiency.
o Monitor and troubleshoot performance issues in data pipelines and data warehouses.
5. Collaboration & Communication:
o Work closely with analysts, and business stakeholders to gather requirements and deliver data solutions.
6. Documentation & Standards:
o Create and maintain comprehensive documentation- we use confluence o Adhere to industry best practices and company standards for data engineering and governance.
Required Skills and Qualifications
• Technical Expertise:
o Proven experience with Data Vault 2.0 methodology.
o Hands-on experience with Snowflake data platform.
o Proficiency in SQL and ETL/ELT processes.
o Experience with data streaming technologies (e.g., Kafka, Kinesis).
• Programming & Tools:
o Strong programming skills in Python, Java, or similar languages.
o DBT hands on experience would be preferred.
• Analytical Skills:
o Strong analytical and problem-solving skills.
o Ability to design scalable and efficient data models.
• Communication:
o Independent minded/self starter- We are a small team, with a lot to work out still.
o Excellent written and verbal communication skills.
o Ability to work effectively in a collaborative team environment.
Would suit a data engineer with good exposure to architecture. Vault exposure within FS would be good.
A pragmatic self starter, who is comfortable working in a small team.
Data Engineer employer: Penna
Contact Detail:
Penna Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer
✨Tip Number 1
Familiarize yourself with Data Vault 2.0 methodology and Snowflake platform. Consider building a small project or contributing to open-source projects that utilize these technologies to showcase your hands-on experience.
✨Tip Number 2
Network with professionals in the data engineering field, especially those who have experience with Data Vault and Snowflake. Attend meetups or webinars to learn from their experiences and potentially get referrals.
✨Tip Number 3
Brush up on your SQL and ETL/ELT processes. You might want to create sample data pipelines or work on case studies that demonstrate your ability to handle data integration and performance optimization.
✨Tip Number 4
Prepare to discuss your collaborative experiences in previous roles. Think of specific examples where you worked closely with analysts or business stakeholders to deliver data solutions, as this will be crucial in our small team environment.
We think you need these skills to ace Data Engineer
Some tips for your application 🫡
Understand the Role: Make sure to thoroughly read the job description and understand the key responsibilities and required skills. Tailor your application to highlight your experience with Data Vault 2.0, Snowflake, and relevant programming languages.
Highlight Relevant Experience: In your CV and cover letter, emphasize your hands-on experience with data integration, ETL/ELT processes, and any projects where you've implemented Data Vault 2.0 architecture. Use specific examples to demonstrate your expertise.
Showcase Analytical Skills: Provide examples of how you've used your analytical and problem-solving skills in previous roles. Mention any experience you have with performance optimization and troubleshooting in data pipelines.
Communicate Effectively: Since excellent communication skills are essential for this role, ensure that your application is well-written and free of errors. Clearly articulate your ability to work collaboratively in a small team environment.
How to prepare for a job interview at Penna
✨Showcase Your Data Vault 2.0 Knowledge
Be prepared to discuss your experience with Data Vault 2.0 methodology in detail. Highlight specific projects where you designed and implemented this architecture, focusing on the challenges you faced and how you overcame them.
✨Demonstrate Technical Proficiency
Make sure to showcase your hands-on experience with the Snowflake data platform and your proficiency in SQL and ETL/ELT processes. You might be asked to solve a technical problem or explain your approach to building data pipelines.
✨Emphasize Collaboration Skills
Since the role requires working closely with analysts and business stakeholders, be ready to share examples of how you've successfully collaborated in previous roles. Discuss how you gather requirements and deliver tailored data solutions.
✨Prepare for Problem-Solving Questions
Expect questions that assess your analytical and problem-solving skills. Think of scenarios where you optimized data models or resolved performance issues in data pipelines, and be ready to explain your thought process.
