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- Tasks: Lead a team to scale data architecture and implement data governance strategies.
- Company: A leading technology firm with a focus on innovation and analytics.
- Benefits: Competitive salary, stock options, private health coverage, and more.
- Why this job: Make a real impact by delivering data-driven insights across the business.
- Qualifications: Expertise in dbt, data modeling, and strong SQL skills required.
- Other info: High ownership role with opportunities for professional growth.
The predicted salary is between 48000 - 72000 £ per year.
A leading technology firm is seeking a passionate Lead Analytics Engineer to guide an Analytics Engineering team in scaling a modern data architecture. This role offers high ownership, demanding strong expertise in dbt and data modeling, along with exceptional SQL skills. You will lead various data governance strategies and act as a strategic partner to commercial teams, ensuring the delivery of data-driven insights across the business.
This position provides a competitive salary and numerous benefits, including stock options and private health coverage.
Lead Analytics Engineer: Scale Data Architecture & dbt employer: Swap
Contact Detail:
Swap Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Analytics Engineer: Scale Data Architecture & dbt
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those already working at the company you're eyeing. A friendly chat can give you insider info and maybe even a referral!
✨Tip Number 2
Show off your skills! Prepare a portfolio or case studies that highlight your expertise in dbt, data modelling, and SQL. This will help you stand out during interviews and demonstrate your hands-on experience.
✨Tip Number 3
Practice makes perfect! Get ready for technical interviews by brushing up on your SQL queries and data governance strategies. Mock interviews with friends or using online platforms can really boost your confidence.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Lead Analytics Engineer: Scale Data Architecture & dbt
Some tips for your application 🫡
Show Your Passion: When writing your application, let your enthusiasm for data architecture and analytics shine through. We want to see how passionate you are about leading a team and driving data-driven insights!
Highlight Your Expertise: Make sure to showcase your strong skills in dbt, data modelling, and SQL. We’re looking for someone who can really demonstrate their technical prowess, so don’t hold back on the details!
Tailor Your Application: Customise your application to reflect the specific requirements of the Lead Analytics Engineer role. We appreciate when candidates take the time to align their experiences with what we’re looking for.
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 this exciting opportunity!
How to prepare for a job interview at Swap
✨Know Your dbt Inside Out
Make sure you’re well-versed in dbt and can discuss its features and benefits confidently. Prepare to share examples of how you've used dbt in past projects, focusing on specific challenges you faced and how you overcame them.
✨Showcase Your SQL Skills
Brush up on your SQL knowledge before the interview. Be ready to solve SQL queries on the spot or discuss complex data models you've built. Practising common SQL problems can help you articulate your thought process clearly.
✨Understand Data Governance
Familiarise yourself with data governance strategies and be prepared to discuss how you’ve implemented these in previous roles. Highlight your experience in ensuring data quality and compliance, as this will resonate with the company’s needs.
✨Be a Strategic Partner
Think about how you can act as a strategic partner to commercial teams. Prepare examples of how your insights have driven business decisions in the past. This will show that you understand the importance of collaboration and data-driven decision-making.
