At a Glance
- Tasks: Design and implement scalable data models and analytics solutions.
- Company: Join Checkout, a leading tech company focused on innovation.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on collaboration and continuous improvement.
- Why this job: Make a real impact by shaping product evolution through data insights.
- Qualifications: Experience in data engineering, SQL, and cloud technologies required.
The predicted salary is between 60000 - 80000 € per year.
As a Data Analytics Engineer at Checkout you will be responsible for enabling key insights on how products are performing and establishing a single source of truth for North Star and tracking metrics, working closely with product managers and product data scientists to shape the product’s evolution at Checkout. You’ll have the opportunity to build new data products and introduce step changes in how we view analytics for these critical areas. You’ll have end-to-end ownership of multiple data products from design to implementation to the operationalisation.
How You’ll Make An Impact
- Design and implement high-performance, reusable, and scalable data models for our data warehouse using dbt and Snowflake
- Design and implement Looker structures (explores, views, etc) which will enable users across the organization to self-serve analytics
- Work closely with data analysts and business teams to understand business requirements and provide data ready for analysis and reporting
- Continuously discover, transform, test, deploy and document data sources and data models
- Apply, help define, and champion data warehouse governance: data quality, testing, documentation, coding best practices and peer reviews
- Take initiative to improve and optimise analytics engineering workflows and platforms
Key Requirements
- Proven delivery experience as a data, business intelligence or analytics engineer
- Hands-on proven data modelling and data warehousing skills demonstrated in large-scale data environments
- Proven experience in software development lifecycle in analytics (e.g. version control, testing, and CI/CD)
- Excellent SQL and data transformation skills (e.g. ideally proficient in dbt or similar)
- Familiarity with at least one of these Cloud technologies: Snowflake, AWS, Google Cloud, Microsoft Azure
- Passionate about sales, finance, customer, marketing and/or product analytics data
- Good attention to detail to highlight and address data quality issues
Senior Analytics Engineer London employer: Checkout Ltd
At Checkout, we pride ourselves on fostering a dynamic and inclusive work culture that empowers our employees to thrive. As a Senior Analytics Engineer in London, you will enjoy competitive benefits, opportunities for professional growth, and the chance to work with cutting-edge technologies in a collaborative environment. Join us to make a meaningful impact on our data-driven decision-making processes while enjoying the vibrant atmosphere of one of the world's leading financial hubs.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Analytics Engineer London
✨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 and analytics projects. This is your chance to demonstrate your hands-on experience with tools like dbt and Snowflake, making you stand out from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your SQL and data transformation skills. Be ready to discuss your past projects and how you've tackled challenges in analytics engineering. Confidence is key!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Senior Analytics Engineer London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Senior Analytics Engineer role. Highlight your experience with data modelling, analytics engineering, and any relevant tools like dbt and Snowflake. We want to see how your skills align with what we're looking for!
Showcase Your Projects:Include specific projects where you've designed and implemented data models or analytics solutions. We love seeing real examples of your work, so don’t hold back on the details that show off your skills and impact!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about analytics and how you can contribute to our team. We appreciate a personal touch, so let your personality come through while keeping it professional.
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 gives you a chance to explore more about us and what we do!
How to prepare for a job interview at Checkout Ltd
✨Know Your Data Tools
Make sure you brush up on your SQL skills and get familiar with dbt and Snowflake. Be ready to discuss how you've used these tools in past projects, as they'll want to see your hands-on experience.
✨Understand the Business Impact
Get a good grasp of how analytics can drive product decisions. Think about examples where your insights led to tangible changes in a product or process. This will show that you understand the bigger picture.
✨Prepare for Technical Questions
Expect questions around data modelling and warehousing. Be prepared to explain your approach to designing scalable data models and how you ensure data quality. Practising common technical interview questions can really help.
✨Show Your Collaborative Spirit
Since you'll be working closely with product managers and data scientists, highlight your teamwork skills. Share examples of how you've successfully collaborated with cross-functional teams to achieve project goals.
