Analytics Engineer

Lovable Lovable · Coding AI · Stockholm, Sweden · Engineering

This role focuses on building and maintaining data models, defining metrics, and ensuring data quality within a data warehouse. It involves transforming raw data into usable datasets and collaborating with domain teams and data platform engineers. The role requires expertise in SQL, dbt, data warehousing concepts, and modern ELT workflows.

What you'd actually do

  1. Build and maintain data models, following modular, tested, and version-controlled practices
  2. Partner with domain teams to understand business logic and codify it into reusable models and metrics
  3. Define and document key metrics and data contracts across domains
  4. Collaborate with Data Platform Engineers to optimize query performance and warehouse cost
  5. Automate and maintain data documentation, lineage, and governance standards

Skills

Required

  • SQL
  • dbt
  • data modeling
  • data warehousing
  • cloud data warehouses (Snowflake, BigQuery, Redshift, Databricks)
  • BI tools (Looker, Tableau, Power BI, Hex, Metabase)
  • dimensional modeling
  • data contracts
  • ELT workflows
  • orchestration workflows (Airflow, Dagster, Prefect)

Nice to have

  • SQLMesh
  • Rust

What the JD emphasized

  • Expertise with SQL, dbt, SQLMesh or similar tools (data modeling, testing, macros, docs)
  • Experience with data warehousing concepts, cloud warehouses (Snowflake, BigQuery, Redshift, Databricks) and BI tools (Looker, Tableau, Power BI, Hex, Metabase, etc)
  • Understanding of dimensional modeling, data contracts, and metrics/semantical layers
  • Familiarity with modern ELT and orchestration workflows (Airflow, Dagster, Prefect, etc)
  • Strong business acumen and ability to translate domain logic into scalable data structures