Resumo da vaga

Staff Data Platform Engineer

Requisitos e responsabilidades

Conteúdo da vaga extraído em seções para revisão mais rápida.

Details

  • Design and evolve data pipeline architecture and storage systems, driving technical direction across the team;
  • Build and deliver high-quality data pipelines end-to-end, from initial design through production deployment;
  • Mentor engineers, raise the technical bar through code reviews, and unblock teammates on complex problems;
  • Own team outcomes — set expectations, ensure delivery quality, and take accountability for results.

Required:

  • 8+ years of hands-on software engineering experience, with significant depth in data pipelines, backend services, or data platform engineering;
  • Deep experience designing and operating data lakes/warehouses/lakehouses at production scale;
  • Experience in scaling data pipelines and good understanding of trade-offs (performance, resources)
  • Expert Python skills: you write clean, performant, testable code and can establish standards for others;
  • Experience across the modern data processing stack e.g., Kafka or Redpanda (real-time ingestion, delivery semantics), Flink or Spark (stream and batch processing, stateful operations), Airflow or Dagster (scheduling, backfills, dependency management);
  • Expert-level SQL: complex joins, query planning and optimization, schema design across both OLTP and OLAP systems;
  • Strong database architecture skills;
  • Solid distributed systems experience;
  • Comfortable leading technical discussions, building consensus on architectural decisions, and being the person the team turns to when things get hard;
  • Team-oriented work and good communication skills are an asset;
  • Proficiency in English.

Would be a plus:

  • Experience working with distributed Clickhouse cluster;
  • Blockchain domain knowledge or an ability of mastering complex technical domains quickly;
  • Track record of driving engineering standards and best practices across a team.

Tech stack:

  • Languages & Frameworks: Python, FastAPI
  • Databases: PostgreSQL, ClickHouse, Redis, Snowflake
  • Infrastructure: Docker, Kubernetes, Terraform, AWS
  • Data Processing: Kafka, Apache Iceberg, Spark
  • Things we'll likely use in the future: Airflow / Dagster / Redpanda

You’ll get:

  • AI tool of your choice: Cursor or Claude Code.
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FocoData Platform EngineeringÁrea da vaga
Sinal de senioridadeSeniorNível do candidato
StackAWS, Docker, KubernetesSkills principais
Localização38 países aceitosElegibilidade

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