Analytics Engineer

TL;DR - We’re looking for analytics engineers to own our warehouse’s semantic and modeling layer, turning raw data into well-defined, trustworthy datasets and metrics. Our internal users are primarily agents: every human queries data through their agent. You care deeply about analytical consistency, meaningful metrics, and building foundations that agents can reliably interpret and use to answer business questions.

Why Lovable?
Lovable is the software creation platform that gives people the power to act on the problems closest to them. For decades, turning an idea into software required so much capital, technical fluency, and time that many ideas never came to life. Lovable is the counterargument: a platform for all people with ideas, ambition, and problems worth solving. From solopreneurs to small business owners to teams at companies like Adidas and Zendesk, people have built over 60 million projects on Lovable since its launch in November 2024. And we’re just getting started.
We’re building a generational company from Stockholm, with growing teams in London, Boston, New York, and San Francisco. Our team is small, talent-dense, and moving quickly, with a culture rooted in extreme ownership, high velocity, and low-ego collaboration. We look for people who care deeply, ship fast, and are eager to make a dent in the world.
Lovable is one of TIME’s 100 Most Influential Companies and has been recognized on the Forbes AI 50 and CNBC Disruptor 50, reflecting our momentum as one of Europe’s fastest-growing AI companies and one of the most ambitious places to build in this next era of software.

What we are looking for:
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

What you will do:
Build and maintain modular, tested, and version-controlled data models that agents can reliably discover, interpret, and query

Partner with domain teams to translate business logic into reusable models and metrics with explicit definitions and clear context

Own the semantic layer, defining consistent metrics and data contracts across domains so agents answer business questions accurately

Collaborate with Data Platform Engineers to optimize query performance and warehouse cost for agent-driven analytics

Automate documentation, lineage, and governance so agents can understand data meaning, provenance, and usage constraints

Establish standards for analytics development, data modeling, and naming conventions that make agent-mediated self-service analytics reliable for everyone

Our tech stack
We're building with tools that both humans and AI love:
Languages: Go, Rust, Python, SQL

Data: ClickHouse, BigQuery, Parquet on object storage

Streaming & orchestration: Pub/Sub, Temporal

Cloud: GCP, Cloudflare, AWS

DevOps & tooling: GitHub Actions, Grafana, OTEL, Terraform

And always on the lookout for what's next!

About your application
Please submit your application in English - it’s our company language so you’ll be speaking lots of it if you join

We treat all candidates equally - if you’re interested please apply through our careers portal