At a Glance
- Tasks: Lead data engineering teams and transform large-scale data projects.
- Company: Join a forward-thinking company in the financial services sector.
- Benefits: Attractive salary, flexible working options, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on innovation and collaboration.
- Why this job: Shape the future of data architecture and make a real impact.
- Qualifications: Expertise in Snowflake, SQL, and cloud-native platforms required.
The predicted salary is between 70000 - 90000 £ per year.
- Required Skills & Experience
- Technical Expertise
- Strong experience in Snowflake architecture and enterprise data platforms.
- Hands‑on expertise with DBT (Cloud/Core).
- Data ingestion tools such as Fivetran, HVR, or similar technologies.
- Advanced SQL and data modelling techniques.
- Experience designing, implementing, and managing cloud-native data platforms on AWS or Azure.
- Strong understanding of modern data warehousing patterns, including Data Vault.
- Strong understanding of modern data warehousing patterns, including Kimball Methodology.
- Strong understanding of modern data warehousing patterns, including Lakehouse Architecture.
- Leadership & Delivery
- Proven experience leading Data Engineering teams and delivering large‑scale data transformation programs.
- Strong stakeholder management skills across Business, Finance, and Technology functions.
- Experience working within Agile and Dev Ops environments, including CI/CD practices and automation.
- Domain Experience
- Experience within Financial Services or Insurance data environments is highly preferred.
- Understanding of financial data models and reporting structures, including Profit & Loss (P&L).
- Understanding of financial data models and reporting structures, including Regulatory Reporting.
- Understanding of financial data models and reporting structures, including Enterprise Reporting Datasets.
- Soft Skills
- Excellent communication and stakeholder engagement capabilities.
- Ability to translate complex technical concepts into clear business value and outcomes.
- Strong analytical and problem‑solving mindset with a focus on continuous improvement.
- Ability to influence decision‑making and drive data strategy initiatives.
- #J-18808-Ljbffr
Snowflake Architect employer: Doist
Brink's is an exceptional employer that fosters a collaborative and innovative work culture, empowering employees to drive global commercial strategies in the ATM lifecycle solutions sector. With a strong focus on professional development and growth opportunities, employees are encouraged to expand their skills while contributing to sustainable growth and operational excellence. Located in a dynamic environment, Brink's offers unique advantages such as a diverse team and the chance to build long-term partnerships with global customers, making it a rewarding place to advance your career.
StudySmarter Expert Advice🤫
We think this is how you could land Snowflake Architect
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Doist!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Snowflake Architect at Doist.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Doist.
✨Apply Directly through Our Website
When you find a suitable opening like Snowflake Architect at Doist, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Snowflake Architect
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Doist, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Doist. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Doist
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Doist!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
