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Senior Data Engineer (Data Warehouse) - Web3

Binance
Global
Full time
Hybrid

Overview

Department

Engineering

Job type

Full time

Compensation

Salary not specified

Location

Global

Company size

Start Up [ <10 employees ]

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Architect and maintain a highly flexible, enterprise-scale data warehouse. Design end-to-end ETL pipelines, optimize performance, and build metadata and quality monitoring frameworks.

Requirements

  • 5+ years of hands-on experience designing and developing data lakes and data warehouse solutions.
  • Deep expertise in data warehouse modeling and governance, including dimensional modeling, information factory (data vault) methodologies and “one data” principles.
  • Proficiency in at least one of Java, Scala or Python, plus strong Hive & Spark SQL programming skills.
  • Practical experience with OLAP engines (e.g., Apache Kylin, Impala, Presto, Druid).
  • Proven track record in building high-throughput batch pipelines on Big Data platforms.
  • Familiarity with core Big Data technologies (Hadoop, Hive, Spark, Delta Lake, Hudi, Presto, HBase, Kafka, Zookeeper, Airflow, Elasticsearch, Redis).
  • AWS Big Data service experience is a plus.
  • Strong analytical and system-design capabilities, with a clear understanding of business requirements and ability to abstract and architect solutions.
  • Collaborative mindset, skilled at building partnerships across teams and stakeholders.
  • Preferred: Experience managing petabyte-scale data in Internet-scale environments and resolving critical production incidents.
  • Bilingual English/Mandarin is required to be able to coordinate with overseas partners and stakeholders.

    • Responsibilities

  • Architect and implement a flexible, scalable data warehouse aligned with company specifications and business requirements, accelerating delivery and reducing redundant development.
  • Design, develop, test, deploy and monitor data models and ETL jobs; rapidly troubleshoot complex issues and optimize calculation logic and pipeline performance.
  • Lead data governance initiatives by building and maintaining metadata management and data quality monitoring systems.
  • Foster technical team growth through mentorship, knowledge sharing and continuous improvement of collective skills.
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