Analytics Engineer

Description

Summary: This role involves designing, building, and maintaining data models, writing efficient SQL, embedding AI/LLMs in analytics workflows, and ensuring data quality while collaborating with stakeholders. Highlights: 1. Design and build clean, well-structured data models to support analytics 2. Shape how AI and LLMs are used within analytics workflows 3. Work with cloud data warehouses and advanced data concepts **How You'll Contribute** * Design \& Build Models: Design, build, and maintain clean, well‑structured data models that support analytics, reporting, and self‑serve use cases. * Write Efficient SQL: Write efficient, scalable SQL to transform raw data into trusted, business‑ready datasets, keeping reusability front of mind. * Embed AI \& LLMs: Help shape how we use AI within our analytics workflows. Think critically about how data structure and metadata can support LLMs in generating accurate, meaningful insights. * Ensure Data Quality: Contribute to improving data quality, testing, and documentation across the analytics layer. * Collaborate with Stakeholders: Work closely with stakeholders and users to understand key questions and ensure data is modelled to answer them effectively. **We're Looking for Someone Who…** * Has a SQL‑First Mindset: You possess a strong, SQL‑first mindset where you instinctively reach for SQL to solve data problems. * Drives Business Outcomes: You have a genuine interest in how data can be used to drive business outcomes, not just how it’s built. * Values Collaboration: You bring a collaborative, friendly approach and willingness to share ideas and learn from others. * Understands Trade‑offs: You have strong data modelling skills, with an appreciation for the trade‑offs between performance, simplicity, and flexibility. **Your Technical Toolkit** * Transformation Frameworks: Proven experience building data models using dbt, SQLMesh, or a similar transformation framework. * Cloud Data Warehouses: Experience working with cloud data warehouses (Snowflake, BigQuery, Redshift, etc.) on large‑scale datasets. * Data Architecture: A solid understanding of data architecture and how different layers of the data stack fit together. * Advanced Data Concepts: Familiarity with semantic layers, metrics layers, or enabling self‑serve analytics, as well as experience designing data models for AI/ML or LLM‑based use cases. **Languages** * Fluent English (mandatory) **Location** * Portugal **Work Model** * Full remote