Get to know our Team:
GrabPay (digital payment wallet for SEA) and GFSA – Grab Financial Services Asia is a recent addition to Grab’s array of product and service offerings focused on the extension of Microcredit to drivers, agents, and merchants in Grab’s ecosystem.
GFSA team is a combination of a strong talent pool and deep local market operators across its focus markets. We are incredibly excited about the opportunity ahead of us.
We are looking to put together the best possible combination of business build drive, industry expertise, and local market depth as part of our team.
GFSA team is responsible for end to end conceptualization, design, development, execution and ongoing management of all lending activities in its focus markets and segments.
Get to know the Role:
As the Software Engineer, Data Services role, you will be working on all aspects of Data, from Platform and Infra build out to pipeline engineering and writing tooling/services for augmenting and fronting the core platform. You will be responsible for building and maintaining the state-of-the-art data Life Cycle management, including acquisition, storage, processing and consumption channels. The team works closely with Data scientists, Product Managers, Legal, Compliance and business stakeholders across the SEA in understanding and tailoring the offerings to their needs. As a member of the Data Services, GrabPay, you will be an early adopter and contributor to various open source big data technologies and you are encouraged to think out of the box and have fun exploring the latest patterns and designs in the fields of Software and Data Engineering.
The day-to-day activities:
- Build and manage Grab’s largest data asset using some of the most scalable and resilient open source big data technologies like Kafka, Yarn, HDFS, ElasticSearch, Presto, HDF and similar
- Design and deliver a next-gen data lifecycle management suite of tools/frameworks, including ingestion and consumption on the top of the data lake to support real-time, API-based and serverless use-cases, along with batch (mini/micro) as relevant
- Liaise with Product, BD and other relevant stakeholders in identifying and coding for various data related quirks like regional legal and regulatory requirements, metadata unification, securing the access and storage through right access control frameworks
- Build and expose metadata catalog for the Data Lake for easy exploration, profiling as well as lineage requirements
- Enable Data Science teams to test and productionize various ML models, including propensity, risk and fraud models to better understand, serve and protect our customers
- Lead technical discussions across the organization through collaboration, including running RFC and architecture review sessions, tech talks on new technologies as well as retrospectives
- Apply core software engineering and design concepts in creating operational as well as strategic technical roadmaps for business problems that are vague/not fully understood
- Obsess security by ensuring all the components, from the platform, frameworks to the applications are fully secure and are compliant by the group’s infosec policies.
The must haves:
- At least 3 to 8 years of relevant application and/or platform development experience of mission-critical systems on Hadoop, Mesos, Kubernetes, Kafka or similar. Candidates will be aligned appropriately within the organization depending on experience and depth of knowledge
- Should be familiar with all Hadoop Ecosystem components and Hadoop Administration Fundamentals
- Good knowledge of Complex Event Processing (CEP) systems like Spark Streaming, Kafka etc
- Strong software engineering background, with good knowledge of algorithms, distributed systems, databases and software engineering.
- Strong hands-on experience in at least one of the programming languages used at Grab - Java, Scala, Python or Go along with a fair understanding of runtime complexities.
- Experience with NoSQL databases – KV/Document/Graph and similar.
- Proven Ability to contribute to open source community and up-to-date with the latest trends in the Big Data Space.
- Good understanding and hands-on OS knowledge especially Linux flavor.
- “Educated” on latest developments in the areas of dev-ops and CI/CD, including containerization, blue-green deployments, 12-factor apps, secrets management etc
- Good understanding of Machine Learning models and efficiently support them is a plus.
