Remote Engineer:in

ALOHAS is more than a fashion brand: it's a responsible shopping experience and an opportunity to take part in the fight against overproduction in the fashion industry.

Working at ALOHAS means being part of the innovation and growth of a Spanish start-up with a young, creative, and proactive team, where communication flows and energy levels are high. It's a demanding environment, but extremely rewarding, and we take pride in learning from one another and strive to make sustainability a way of life beyond the workplace.

We are passionate about what we do, and that passion shines through in our work. THE ROLEWe are looking for an Analytics Engineer to help shape how ALOHAS understands its own performance: from the way we define a metric, to the way it lands in a Lightdash dashboard, to the way an AI agent reasons over it. You will sit at the heart of our modern data stack (BigQuery + dbt + Lightdash), partnering with our Data Engineer on the technical side and with any team across the business on the stakeholder side.

Your work will turn raw, well-governed data into the facts, marts, and metric definitions the company runs on day to day. This is a builder role You will own questions end-to-end, from "what does this number mean?" to "here it is, trusted, documented, and visible to anyone who needs it".You will be joining a platform that is already in production: a growing dbt project in BigQuery (staging, intermediate, and mart layers), Airbyte syncing from our core source systems (Shopify, Odoo, carrier billing, and more), and Lightdash already used by stakeholders across the business. YOUR CHALLENGEOur modern data platform runs on BigQuery as the warehouse, dbt Core (with dbt Cloud for CI/CD) as the transformation layer, and Lightdash as the visualization and semantic layer.

Ingestion is handled by Airbyte and orchestration by Dagster. Day-to-day you will write SQL and Python, ship through Git/GitHub, and feed the metrics you build into our Claude-based AI agents and skills. Business ModelingBuild the trusted data models the business runs on every day — the facts, dimensions, and curated marts in dbt that turn well-prepared raw data into the numbers any team across the company actually uses.

Translate business questions into clean, well-named models across the full business — sales, retail, supply chain, distribution, marketing, inventory, returns, and customer behavior.

Write tests, documentation, and exposures so downstream consumers can trust what they read.

Work within the dbt project conventions our Data Engineer maintains — layered structure (staging / intermediate / marts), naming, testing, and documentation standards — so the platform stays consistent as it scales.

Metrics & Semantic LayerOwn the canonical metric definitions across the business — every KPI that matters, from the handful leadership tracks weekly to the long tail of operational and behavioral metrics each team depends on. Build and maintain the semantic layer in Lightdash so every team works from the same definitions.

Partner with stakeholders across the business to align metrics with how the company actually operates.

Self-Serve AnalyticsDesign and own Lightdash explores and dashboards that replace one-off reporting requests with governed, self-serve views.

Help stakeholders shift from asking for numbers to exploring them directly. AI-Ready DataEnforce that the metrics and models you build are the canonical source of truth for all downstream consumers, ensuring our Claude-based agents and skills reason over the same semantic layer used for dashboards.

Help close the gap between human-facing dashboards and AI-facing data products.

Team ContributorBe a generous teammate. When more experienced members of the team surface an issue or kick off a new initiative, you will be ready to jump in, contribute, and learn alongside them.

Bring your perspective to design discussions and reviews and stay open to learning from others YOU WILL ROCK AT THIS ROLE IF YOU HAVE...Experience & BackgroundHands-on experience with SQL + dbt — modular models, tests, docs, exposures, macros, packages, and the full toolkit.

Fluency in business and financial metrics across the board: commercial, unit economics, marketing, retention, operations, customer experience. You can challenge a metric definition, not just code one.

Experience with a modern data warehouse — BigQuery preferred, but Snowflake / Redshift / Databricks experience translates. Comfort with cost- and performance-aware querying (partitioning, clustering, query optimization) at scale.

Data quality discipline — source freshness, tests beyond unique/not_null, anomaly detection, and a habit of writing documentation as you build.

An AI-native way of working: you already use Claude, Opencode, Cursor, or similar in your day-to-day for writing SQL, debugging, documen