Data Warehouse Engineer

About RedotPay

RedotPay 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. RedotPay 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.