Design and implement solutions for the platform's most complex challenges — from high-scale pipeline architecture to modeling decisions that impact multiple consuming teams;
Lead code reviews with technical depth, highlighting trade-offs in performance, cost, and maintainability;
Serve as a technical point of reference: investigate production inconsistencies and optimize cloud costs;
Collaborate with partner teams to define how data reaches models — discussing data contracts, latency, quality, and governance;
Identify significant technical debt, recommend when to address it, and lead the remediation;
Work in an AI-driven environment where we actively use AI to raise the team's productivity — and contribute ideas on how this applies to data engineering.

Python, Scala, Java, or Go;
SQL;
Algorithms, data structures, and application scalability;
Distributed processing frameworks such as Apache Spark or Apache Beam;
Containerization and deployment using Docker and Kubernetes;
CI/CD;
Platform monitoring and observability;
Pipeline orchestration with Airflow, dbt, or Dataform;
Google Cloud Platform solutions (e.g., BigQuery, GCS, GKE, IAM, Dataproc, Cloud SQL, Composer, Pub/Sub, Dataflow);
Management and support of analytical visualization platforms (Metabase, Looker, Tableau)

Senior Data Platform Engineer

Key skills