Data Engineer, M-DAQ
About M-DAQ Pte. Ltd.
M-DAQ builds over-the-top (OTT) applications to facilitate cross-border business for various industries. These include securities markets, e-Commerce platforms and payment solutions providers. M-DAQ achieves this through our proprietary technology, together with the remittance license awarded by the Monetary Authority of Singapore. M-DAQ was awarded “Best Tech Company To Work For 2019” by Singapore Computer Society (SCS).
M-DAQ is a high-growth FinTech start-up that focuses on proprietary best-in-class corporate FX solutions across Asia. We have to date processed over $10 billion in FX transactions and generated hundreds of millions of dollars in revenue for our partners and savings for their end-customers. Having recently concluded our Series D financing round, we have exciting plans to leverage our FX expertise in even more verticals.
Why Us?
- Have a positive impact to the world’s economy by creating a World without Currency BordersTM
- Team Innovation Mindset, People-Oriented
- Challenging environment, offering great opportunities to learn and grow
- Creative and Innovative Workplace
Roles & Responsibility:
- Design, develop and implement Big Data platforms in cloud
- Build a centralized datalake and data warehouse for data feeds from all M-DAQ entities
- Set up tools for data ingesting (batch and real-time), data cleaning, building data cubes in the data warehouse
- Build tools to monitor data quality and manage data changes
- Build end-user facing data catalogue and data lineage to facilitate data discovery
Requirements:
- 5+ years of experience with excellent coding skills (Python, Java or Scala)
- Bachelor or Master’s degree in Computer Science, Engineering, or relevant industry experience is required
- Experience with Big Data technology architecture (Hadoop, Spark, Kafka, Flink, Hive etc), tuning, troubleshooting and scaling the systems. Deep understanding with the internal principles of these systems.
- Hands on experience designing and implementing Data Warehouse (Snowflake, Athena or Redshift)
- Solid understanding of ETL and ELT approaches and large scale data ingestion/integration framework.
- Experience in designing data quality morning and fault-tolerant pipelines
- Hands on experience in developing/designing/integrating using Kubernetes
- Exposure in building real-time processing pipelines using Flink or Spark-streaming
- Exposure to data and machine learning services from Amazon Web Services (AWS) and/or Google Cloud (GCP) is a plus
What We Offer: