Data Warehouse Engineer

• About Redot
• Pay
• Redot
• Pay is a global crypto payment fintech integrating blockchain solutions into traditional banking and finance infrastructure.
• Our user-friendly crypto platform empowers millions globally to spend and send crypto assets, ensuring faster, more accessible, and inclusive financial services.
• Redot
• Pay advances financial inclusion for the unbanked and supports crypto enthusiasts, driving the global adoption of secure and flexible crypto-powered financial solutions.
• Join us in shaping the future of finance and making a meaningful impact on a global scale.

Job Description

We are looking for an experienced, self-driven Data Warehouse Engineer. You will be responsible for the architectural design of the company's core data warehouse, data model development, and optimization of ETL data pipelines.

Responsibilities

• Data Warehouse Modeling & Development: Design and develop the company-level data warehouse models, including building the ODS, DWD, DWS, and ADS layers, ensuring the data models are scientific, stable, and scalable.ETL Pipeline Development: Build efficient and stable ETL/ELT data processing workflows, write high-quality data processing scripts, and ensure timely data delivery (SLA compliance).
• Business Data Support: Deeply understand the business, collaborate closely with Product, Operations, BI, and Data Analytics teams, accurately capture data requirements, and provide agile data mart support and metric system development.
• Performance Tuning & Maintenance: Perform SQL optimization and Hive/Spark job performance tuning in large-scale data environments, resolving issues such as data skew, scheduling delays, and resource waste.
• Data Quality & Governance: Participate in the construction of data quality monitoring systems (DQC), manage metadata, map data lineage, ensure consistency of data definitions, and maintain the accuracy of data assets.

Requirements

• Bachelor’s degree or above in Computer Science, Mathematics, Statistics, or related fields.3+years of experience in data warehousing or big data development.
• Solid theoretical foundation in data warehousing, with a deep understanding of dimensional modeling
• Proficient in designing fact tables, dimension tables, Slowly Changing Dimensions (SCD), and subject domains.
• Excellent SQL writing and extreme performance tuning skills.
• Proficient in Hive, Spark, and other big data computing frameworks.
• Familiar with the working principles of HDFS and YARN.Good to have at least one mainstream big data scheduling system such as Dolphin Scheduler, Airflow, or Azkaban.