Crystal Intelligence
Staff Data Platform Engineer
Remote Data Platform Engineering role with clear candidate location fit.
PostedJun 11, 2026
Eligible countries38 accepted countries
Seniority signalSenior
Work settingRemote
Accepted candidate locations
Role overview
Staff Data Platform Engineer
Requirements and responsibilities
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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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