СШАGemini
Senior Data Engineer
2 нед. назад
126k–180k USD / годСШАSeniorRemote
pythonsqlsparkflinkbeamdatabricksbigquerysnowflake
Gemini is a global crypto and Web3 platform offering crypto products and services in 70+ countries. The Data Team designs and operates data infrastructure to support business analytics and machine learning.
О компании
- 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.
О продукте
- The Data Team designs and operates the data infrastructure that powers insight, reporting, analytics, and machine learning across the business.
Обязанности
- 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
Требования
- 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