Data Warehouse Engineer — ETL & Analytics

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 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.