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- Tasks: Transform data into insights to drive decisions across various teams.
- Company: Gaia, a pioneering family building company with a focus on analytics.
- Benefits: Competitive salary, dynamic work environment, and opportunities for growth.
- Why this job: Make a real impact by shaping data-driven decisions in a fast-paced setting.
- Qualifications: 3+ years in analytics, strong SQL skills, and familiarity with BI tools.
- Other info: Join a collaborative team that embraces innovation and values your insights.
The predicted salary is between 53000 - 57000 £ per year.
Location: London or South America
Experience: 3+ years experience in a similar role.
The role: Gaia is building the category-defining family building company. We are looking for a company-wide Data Analyst to serve as the analytical spine of the company, turning data into insights to better inform decisions across Product, Growth, Operations, and Finance. This is a high-impact, hands-on role. You will own Gaia’s source of truth, proactively surface insights, and raise the bar on how the company uses data to operate, prioritize, and scale.
What will you own?
- Company-wide analytics and source of truth data for our BI tool
- Responsible for the delivery and usage of the data pipeline, including how the data is modelled
- Own and maintain core dashboards and metrics
- Ensure data is accurate, trusted and decision ready through daily monitoring - this includes data backfill when needed
- Define and evolve the metrics used to run Gaia
- Surface leading indicators
- Partner with product and engineering to ensure data requirements are taken into account before development work happens, defining the right questions, surfacing insights and supporting decision making
- Modelling, monitoring, experimenting and forecasting
Who are we looking for?
- Former Growth, Ops, or BizOps analysts who want to move closer to real decisions and accountability
- Analysts who have sat in high-velocity teams to understand funnels, unit economics, trade-offs, and constraints.
- Comfortable with our current data stack of Python, SQL, dbt, Prefect, Omni (or a similar BI tool) and Fivetran
- You are data curious, and won’t hesitate to challenge business decisions with relevant insights
- You know how to balance speed and thoroughness - you like going down data rabbit holes to deeply understand a problem but also know when to make do with available data to make quicker decisions when needed
Technical Competencies Essential
- SQL
- Familiarity with any modern BI tool: Omni, Looker, Metabase, Tableau etc
- Excel
- Python
Nice to have
- dbt
- Data warehouse experience: snowflake, big query, redshift, databricks etc
- Use of data extraction tools: fivetran, air byte, stitch etc
- Use of data orchestration tools: Airflow, Prefect, Dagster etc
- Familiar with HubSpot
What Success Looks Like
- High data richness, quality and accuracy
- Consistent metrics used to make decisions
- Faster, higher-quality decisions across Product, Growth, Finance and Operations
- Clear visibility into funnel health, unit economics, and operational leverage
- Fewer surprises, earlier course-correction
How do we work
- We move fast and ship several times a day
- We collaborate closely with business functions and tech early and frequently
- We use a modern data stack
- We keep things simple while always ready to embrace the latest technologies when relevant
Compensation Range: £53K - £57K
Analytics Engineer employer: Gaia
Contact Detail:
Gaia Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Analytics Engineer
✨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 projects, dashboards, and any cool insights you've uncovered. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills. Be ready to discuss SQL queries, data modelling, and how you've used analytics to drive decisions in past roles. Practice makes perfect!
✨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
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the role of Analytics Engineer. Highlight your experience with SQL, Python, and any BI tools you've used. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Share your passion for data and how you’ve used it to drive decisions in previous roles. Let us know why you’re excited about joining Gaia and how you can contribute.
Showcase Relevant Projects: If you've worked on projects that involved data modelling or analytics, don’t hold back! Include links or descriptions of your work that demonstrate your ability to turn data into actionable insights. We love seeing real examples!
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 get the best experience possible. Plus, we can’t wait to see your application!
How to prepare for a job interview at Gaia
✨Know Your Data Stack
Familiarise yourself with the tools mentioned in the job description, like SQL, Python, and any BI tools. Be ready to discuss how you've used these in past roles, as this will show your technical competence and readiness for the role.
✨Prepare Insightful Questions
Think about the key metrics and insights that would be valuable for Gaia. Prepare questions that demonstrate your understanding of data-driven decision-making and how you can contribute to improving their analytics processes.
✨Showcase Your Analytical Mindset
Be prepared to discuss specific examples where you've turned data into actionable insights. Highlight your experience in high-velocity teams and how you've balanced speed with thoroughness in your analyses.
✨Emphasise Collaboration Skills
Since the role involves partnering with product and engineering teams, share experiences where you've successfully collaborated across departments. This will illustrate your ability to work in a fast-paced, team-oriented environment.
