Analytics Engineer

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?

1. Company-wide analytics and source of truth data for our BI tool

   1. Responsible for the delivery and usage of the data pipeline, including how the data is modelled

   2. Own and maintain core dashboards and metrics

   3. Ensure data is accurate, trusted and decision ready through daily monitoring - this includes data backfill when needed

   4. Define and evolve the metrics used to run Gaia

   5. Surface leading indicators

2. 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

3. 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 decision 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 haves

• dbt

• data warehouse experience: snowflake, big query, redshift, databricks etc

• Use of data extractions 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