Senior Data Engineer

Gemini is a global crypto and Web3 platform founded by Cameron and Tyler Winklevoss in 2014, offering crypto products and services to individuals and institutions in over 70 countries. The Data Team designs and operates the data infrastructure that powers insight, reporting, analytics, and machine learning across the business.

$126,000 – $180,000 (base salary range, New York)

☑️ Чем предстоит заниматься

• Design, build, and maintain data infrastructure and pipelines spanning both batch and real-time / streaming workloads, contributing to architectural decisions along the way
• Build and maintain scalable, efficient, and reliable ETL/ELT pipelines using languages and frameworks such as Python, SQL, Spark, Flink, Beam, or equivalents
• Work on real-time or near-real-time data solutions (e.g. CDC, streaming, micro-batch) for use cases that require timely data
• Partner with data scientists, ML engineers, analysts, and product teams to understand data requirements, define SLAs, and deliver coherent data products that others can self-serve
• Establish data quality, validation, observability, and monitoring frameworks (data auditing, alerting, anomaly detection, data lineage)
• Investigate and resolve complex production issues: root cause analysis, performance bottlenecks, data integrity, fault tolerance
• Document data flows, data dictionaries, architecture patterns, and operational runbooks

☑️ Наши пожелания к кандидатам

• 5+ years of experience in data engineering (or similar) roles
• Strong experience in ETL/ELT pipeline design, implementation, and optimization
• Deep expertise in Python and SQL writing production-quality, maintainable, testable code
• Experience with large-scale data warehouses (e.g. Databricks, BigQuery, Snowflake)
• Solid grounding in software engineering fundamentals, data structures, and systems thinking
• Hands-on experience in data modeling (dimensional modeling, normalization, schema design)
• Experience building systems with real-time or streaming data (e.g. Kafka, Kinesis, Flink, Spark Streaming), and familiarity with CDC frameworks
• Experience with orchestration / workflow frameworks (e.g. Airflow)
• Familiarity with data governance, lineage, metadata, cataloging, and data quality practices

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