Data Warehouse Engineer

RedotPay; Hong Kong; On-site; HKD 420,000 - 700,000; Full time

Job summary

RedotPay is seeking an experienced Data Warehouse Engineer responsible for architecting the core data warehouse, developing data models, and optimizing ETL pipelines. The role requires strong expertise in dimensional modeling, SQL performance tuning, and big data frameworks such as Hive and Spark.

You will collaborate with Product, Operations, BI, and Data Analytics teams to capture data requirements, ensure data quality, and deliver scalable data solutions across the organization.

Qualifications

• Bachelor’s degree or above in Computer Science, Mathematics, Statistics, or related fields.
• 3+ years of experience in data warehousing or big data development.
• Strong foundation in dimensional modeling and data warehousing concepts.
• Proficient in designing fact/dimension tables, SCDs, and domain modeling.
• Excellent SQL writing and performance tuning in large-scale environments.
• Familiar with Hive, Spark, and big data computing concepts.

Responsibilities

• Design and develop the company-level data warehouse architecture and models (ODS, DWD, DWS, ADS).
• Build and optimize ETL/ELT data pipelines with reliable data delivery and SLA adherence.
• Collaborate with Product, Operations, BI, and Analytics to capture data requirements and support metrics.
• Tune SQL and Spark/Hive jobs to address data skew, delays, and resource inefficiencies.
• Contribute to data quality, governance, and metadata management efforts.

Skills

Data Warehousing

SQL

ETL Pipelines

Dimensional Modeling

SCD

Data Governance

Data Quality

Big Data

Data Modeling

Education

Bachelor's degree or above in CS/Math/Statistics

Tools

Hive

Spark

HDFS

YARN

Job description

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.