Data Engineer / Analytics Engineer

We're looking for an engineer to own the data layer at Severex: pipelines, dbt models, source integrations, data quality, and everything that makes them reliable and timely. You'll be the only engineer on the analytics team — we need someone who works autonomously, spots problems before anyone reports them, and fixes root causes rather than symptoms.

What are you working on?

• Platforms: Mobile

For which tasks (responsibilities)?

• Design, build, and maintain dbt models and data marts in Snowflake / BigQuery — architecture, refactoring, testing, documentation.

• Prepare the data infrastructure for an AI analytics agent: maintain a data catalog and semantic layer (table/column descriptions, metric definitions, entity relationships), enforce consistent naming and documentation across dbt models, set up read-only interfaces for automated queries.

• Own pipelines from requirements to production. That means talking to stakeholders, building the thing, and then making sure it actually works reliably every day — not handing it off and moving on.

• Own data quality: validation, anomaly monitoring, data contracts. 

• Proactively find and fix bottlenecks in the infrastructure — cost, speed, reliability.

What kind of professional are we looking for?

• Production experience with dbt. This is a key requirement.

• Strong SQL: window functions, complex transformations, query optimization. Experience with Snowflake or BigQuery is a must.

• Solid Python for data engineering: ETL/ELT pipelines, API integrations, automation.

• Experience with Airflow (or a similar orchestrator), Git, and code review. Understanding of CI/CD, testing, and documentation practices for analytics code.

• Ability to own a pipeline across its full lifecycle — from design to monitoring and incident response in production.

• Engineering maturity and independence: gather requirements, make technical decisions, turn one-off requests into reliable, scalable solutions. 

• Experience with the GCP stack (BigQuery, Cloud Run, GCS).

• Experience with or strong understanding of mobile app analytics: attribution, in-app events, and monetization.
  
  Nice to have

  • Experience migrating between DWH platforms.

  • Interest or experience with LLM/AI tooling in a data context (MCP, semantic layer, data catalog).

  • Experience building a DWH architecture from scratch or doing a major refactor of an existing one.

What are the conditions and bonuses?

• A competitive compensation package

• The remote work model enables you to work from anywhere in the world.

• Health insurance to support your well-being and keep you healthy.

• Continuous professional development and a pathway for career growth.