Join a platform engineering team responsible for the scalable data infrastructure that underpins both product and analytics workloads globally.
Work spans distributed processing, streaming pipelines, orchestration, data quality, and observability — with full ownership of the components you build.
Collaborate closely with Data and ML Engineers and work within a modern lakehouse architecture.
CI/CD, IaC, and engineering best practices are first-class concerns, not aspirations.

3–8 years of hands-on experience in data pipelines and cloud data infrastructure (Mid: 3–5 yrs / Senior: 5–8 yrs)
Python at production-grade, advanced level — not scripting, engineering
SQL with strong command of complex queries and performance tuning
Hands-on experience with Apache Spark or Apache Flink for distributed data processing
Kafka or equivalent streaming platform experience in production environments
Solid experience with at least one major cloud data platform: AWS, GCP, or Azure
Proficiency with a data lakehouse or warehouse technology: Databricks, Snowflake, BigQuery, or Redshift
Pipeline orchestration with Apache Airflow, Prefect, or Dagster
CI/CD for data workflows: dbt, Git, and automated testing practices
Infrastructure as Code with Terraform or Pulumi
Hands-on experience with data quality and observability tooling (Great Expectations, Monte Carlo, or equivalent)
English: B2 (Upper Intermediate) — mandatory
Based in Portugal — mandatory. Portuguese nationals or residents strongly preferred.

Data Platform Engineer – Spark, Kafka, Databricks

Key skills