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- Tasks: Own the analytics engineering function and shape best practices for data analytics.
- Company: Join an innovative platform connecting travellers with reliable home and pet care services.
- Benefits: Enjoy remote work flexibility and be part of a purpose-driven team.
- Why this job: Make a real impact in a growing company focused on shared value and community.
- Qualifications: Strong SQL skills, experience with dbt, and familiarity with cloud data warehouses required.
- Other info: Ideal for those passionate about data transformation in a high-growth environment.
The predicted salary is between 40000 - 60000 £ per year.
This innovative, subscription-based platform brings together a global community of individuals with complementary needs: those seeking flexible travel opportunities and those requiring reliable care for their home and animals. The model is built around trust and mutual benefit, offering a unique alternative to traditional services by focusing on shared value rather than direct transactions.
The company is at an exciting stage of data transformation. Over the past six months, they’ve built the foundations of a modern data platform, including hiring a data manager to oversee governance and integrations, a data engineer, and most recently, a data scientist. With dbt introduced as part of a broader tech overhaul, the team is now looking for a dedicated Analytics Engineer to take ownership of the dbt layer and play a key role in shaping analytics best practices across the business.
As the team scales, there’s a clear need for someone to focus solely on the analytics engineering function. Currently, analytics work is shared across the team, but performance bottlenecks, a lack of consistency, and the opportunity to implement best practice modelling highlight the need for a specialist. This role will be instrumental in building a reliable and scalable production environment on top of a growing set of raw data sources.
Tech Stack and Environment
- Data Warehouse: Redshift
- Modelling: dbt (recently onboarded, still early-stage)
- ETL & Integration: Fivetran, with plans to scale
- Architecture: Medallion
- Dashboarding & Visualisation: Mode Analytics (SQL-based)
Ideal Background:
- Strong SQL skills with hands-on experience in dbt and modern cloud data warehouses (e.g. Redshift, Snowflake, BigQuery)
- Familiarity with ELT tools (e.g. Fivetran), BI tools (e.g. Looker, Tableau, Power BI), and Git-based workflows
- Solid understanding of data modelling, warehousing principles, and analytics best practices
- Experience working cross-functionally with strong communication skills
Nice to have:
- Python, AWS/GCP/Azure, Medallion Architecture, Databricks, and knowledge of data governance or experience in high-growth environments
This is a fantastic opportunity to join a purpose-led business in a high-impact role, helping to shape the data strategy of a company making travel more accessible and pet care more personal.
Analytics Engineer employer: LinkedIn
Contact Detail:
LinkedIn Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Analytics Engineer
✨Tip Number 1
Familiarise yourself with dbt and its best practices, as this role specifically requires ownership of the dbt layer. Consider contributing to open-source dbt projects or creating your own models to showcase your skills.
✨Tip Number 2
Network with professionals in the analytics engineering field, especially those who have experience with Redshift and Fivetran. Engaging in relevant online communities or attending industry meetups can help you gain insights and potentially get referrals.
✨Tip Number 3
Prepare to discuss your experience with data modelling and warehousing principles during interviews. Be ready to share specific examples of how you've implemented analytics best practices in previous roles.
✨Tip Number 4
Showcase your communication skills by preparing to explain complex technical concepts in simple terms. This will demonstrate your ability to work cross-functionally, which is a key requirement for this position.
We think you need these skills to ace Analytics Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your strong SQL skills and hands-on experience with dbt and cloud data warehouses. Emphasise any relevant projects or roles that showcase your ability to work cross-functionally and implement analytics best practices.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company's mission. Discuss how your background aligns with their needs, particularly in building a reliable and scalable production environment using their tech stack.
Showcase Relevant Experience: When detailing your experience, focus on specific examples where you've used ELT tools like Fivetran or BI tools such as Tableau or Power BI. Highlight any instances where you improved data modelling or warehousing principles in previous roles.
Proofread and Edit: Before submitting your application, carefully proofread your documents for any spelling or grammatical errors. Ensure that your application is clear, concise, and free of jargon, making it easy for the hiring team to understand your qualifications.
How to prepare for a job interview at LinkedIn
✨Showcase Your SQL Skills
As an Analytics Engineer, strong SQL skills are crucial. Be prepared to discuss your experience with SQL in detail, including specific projects where you've used it to solve complex problems or improve processes.
✨Demonstrate Your Knowledge of dbt
Since the company is looking for someone to take ownership of the dbt layer, make sure to highlight your hands-on experience with dbt. Discuss how you've implemented best practices in previous roles and any challenges you've overcome while using it.
✨Understand the Tech Stack
Familiarise yourself with the company's tech stack, especially Redshift and Fivetran. Be ready to explain how you've worked with similar tools and how you can contribute to building a reliable and scalable production environment.
✨Communicate Cross-Functionally
Strong communication skills are essential for this role. Prepare examples of how you've successfully collaborated with different teams in the past, and be ready to discuss how you would approach working cross-functionally in this new position.
