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- Tasks: Own data management and ensure accurate analytics for impactful decision making.
- Company: Leading analytics company with a focus on innovation and collaboration.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Why this job: Make a real impact on business functions through data-driven insights.
- Qualifications: 3+ years of experience in SQL, Python, and modern BI tools.
- Other info: Join a dynamic team passionate about data and its influence on business.
The predicted salary is between 30000 - 50000 £ per year.
A leading analytics company is seeking an experienced analyst to own company-wide data management, ensuring accurate analytics for decision making.
Responsibilities include:
- Monitoring data quality
- Collaborating closely with product and engineering teams to define data needs
Ideal candidates will have:
- 3+ years of experience
- Proficiency in SQL, Python, and modern BI tools
- A curiosity for data that influences business decisions
This position offers the opportunity to significantly impact various business functions.
Analytics Engineer: Data Pipelines & Decision Metrics employer: Gaia Family Inc
Contact Detail:
Gaia Family Inc Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Analytics Engineer: Data Pipelines & Decision Metrics
✨Tip Number 1
Network like a pro! Reach out to folks in the analytics field on LinkedIn or at industry events. 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 data projects, especially those involving SQL and Python. This gives potential employers a taste of what you can do and how you think.
✨Tip Number 3
Prepare for interviews by brushing up on common analytics scenarios. Think about how you’d tackle data quality issues or collaborate with product teams. We want to see your problem-solving skills in action!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Analytics Engineer: Data Pipelines & Decision Metrics
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your experience with SQL, Python, and BI tools in your application. We want to see how your skills can help us improve our data management and analytics.
Be Curious: Let us know about your curiosity for data! Share examples of how you've used data to influence business decisions in the past. This will show us that you’re a great fit for our team.
Tailor Your Application: Don’t just send a generic application. Take the time to tailor your CV and cover letter to our job description. We love seeing candidates who take the extra step to connect their experience with what we’re looking for.
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of applications and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at Gaia Family Inc
✨Know Your Data Tools
Make sure you brush up on your SQL, Python, and any BI tools you've used. Be ready to discuss specific projects where you've applied these skills, as this will show your hands-on experience and how you can contribute to the company's data management.
✨Understand the Business Impact
Familiarise yourself with how data influences decision-making in the company. Think about examples from your past work where your analytics led to significant business outcomes. This will demonstrate your understanding of the role's importance.
✨Prepare for Technical Questions
Expect technical questions that test your knowledge of data pipelines and quality monitoring. Practice explaining complex concepts in simple terms, as you'll need to collaborate with product and engineering teams who may not have a deep analytics background.
✨Show Your Curiosity
During the interview, express your curiosity about data and how it drives business decisions. Ask insightful questions about the company's data strategy and challenges they face. This will highlight your passion for the field and your eagerness to contribute.
