Data Engineer - MoneyLion

FinTech Collective, LLC; Kuala Lumpur; On-site; MYR 120,000 - 180,000; Full time

Job summary

MoneyLion's Kuala Lumpur office houses a data engineering team focused on building a scalable, AI-native analytics platform. The Data Engineer will design and optimize Snowflake-centric pipelines, coupled with modern data stack components, to support business decisions with reliable, fast queries.

You will collaborate with data scientists, product managers and engineers to deliver data solutions, govern data assets, and drive cost efficiency while mentoring junior engineers in a fast-paced

Qualifications

• Bachelor's degree in computer science, Data Engineering, or related technical field.
• 1-4 years of hands-on data engineering experience in large-scale, high-volume environments.
• Expert-level proficiency in SQL optimization for multi-petabyte datasets and advanced Python programming.
• Experience with Snowflake architecture, optimization, and cost management at enterprise scale.
• Demonstrated expertise building Kimball dimensional data warehouses and star schema architectures, or equivalent experience with thoughtful, non-generic projects.
• Advanced experience with AWS cloud infrastructure and either DBT or SQLMesh for data transformations.
• Familiarity with AWS is a big plus.
• Experience in planning day to day tasks, knowing how and what to prioritise and overseeing their execution.

Responsibilities

• Extensive expertise operating & optimizing a modern data warehousing solution (Snowflake preferred) from both performance and cost perspectives.
• Expertise with Apache Kafka, real-time data processing, and event-driven architectures
• Collaborate with data scientists, business analysts, product managers, software engineers and other data engineers to develop, implement and validate deployed data solutions.
• Build and maintain data pipelines from internal databases and SaaS applications
• Implement better solutions for data governance at scale, encompassing lineage, discoverability, and regulatory compliance.
• Advocate best practices, uphold the highest standards for code maintainability and performance, and constantly champion our team’s core values of psychological safety and building in public.
• Lead the team in honing our ways of working, coaching and mentoring junior engineers, and managing senior stakeholders from external departments.

Skills

SQL optimization

Python programming

Dimensional data modeling

Data pipelines

Snowflake architecture

AWS cloud

Leadership

Stakeholder management

Education

Bachelor's degree in computer science or related field

Tools

Snowflake

Apache Kafka

Apache Iceberg

DBT

SQLMesh

Python

Job description

About the Role

The Kuala Lumpur office is the technology powerhouse of MoneyLion, where we measure anything and everything and rely on data to guide our decisions: both long-term strategies and day-to-day operations.

Combine that backdrop with an incredible volume and sophistication of data and mix in MoneyLion’s voracious appetite for adopting paradigm-shifting technologies and you have the mission and mandate of the Data Engineering team. Now turbo-charged with a planetscale mandate as part of Gen Digital, we aim to leverage tried-and-true methodologies with game-changing technology to create a AI-native, scalable, extensible, and cost-effective data infrastructure across the Gen Digital enterprise and beyond.

As a Data Engineer, you are a technical Individual Contributor (IC) figure within the team that will design, implement and optimize a Snowflake-centric architecture that will leverage many components of the modern data stack. You will be a trusted subject matter expert on dimensional data modeling, performant SQL query writing, infrastructure optimization, process automation and governance. You will be given the opportunity to leverage the latest and greatest AI tools and features, including Claude Code (Enterprise) and Snowflake’s Cortex Code, among many others.

You will be responsible for designing and implementing an analytical environment using in-house and third-party tools, using Python and/or Java to automate data activities and enable efficient processing of data that is growing in both volume and complexity.

You will design and implement complex data pipelines and data models for analytical consumption.

You will write scalable and performant SQL queries running over billions of rows of data and design workflows aimed to ruthlessly streamline the governance of our complex layers of storage. You should have deep experience in designing and managing large datasets and pipelines to enable business use-cases. You should be an authority at designing, implementing and operating solutions that are scalable, stable and cost-efficient.

Above all, you will be an authority on crafting thoughtful solutions that will empower absolutely everyone at Gen Digital towards an agentic AI‑first analytics and development lifecycle.

Key Responsibilities

• Extensive expertise operating & optimizing a modern data warehousing solution (Snowflake preferred) from both performance and cost perspectives.

• Expertise with Apache Kafka, real‑time data processing, and event‑driven architectures
• Experience with Apache Iceberg, data lakehouse architectures, and modern table formats

• Collaborate with data scientists, business analysts, product managers, software engineers and other data engineers to develop, implement and validate deployed data solutions.

• Build and maintain data pipelines from internal databases and SaaS applications

• Implement better solutions for data governance at scale, encompassing lineage, discoverability, and regulatory compliance.

• Advocate best practices, uphold the highest standards for code maintainability and performance, and constantly champion our team’s core values of psychological safety and building in public.

• Lead the team in honing our ways of working, coaching and mentoring junior engineers, and managing senior stakeholders from external departments.

About You

• Bachelor's degree in computer science, Data Engineering, or related technical field
• 1-4 years of hands‑on data engineering experience in large‑scale, high‑volume environments
• Expert‑level proficiency in SQL optimization for multi‑petabyte datasets and advanced Python programming
• Experience with Snowflake architecture, optimization, and cost management at enterprise scale
• Demonstrated expertise building Kimball dimensional data warehouses and star schema architectures, or equivalent experience with thoughtful, non‑generic projects
• Advanced experience with AWS cloud infrastructure and either DBT or SQLMesh for data transformations
• Familiarity with AWS is a big plus
• Experience in planning day to day tasks, knowing how and what to prioritise and overseeing their execution

What's Next...

After you submit your application, you can expect the following steps in the recruitment process:

1. Online Preliminary Codility test
2. Recruiter Screening Call
3. Take‑home Assessment
4. Take Home Discussion (Virtual)
5. Interview - Hiring Manager (Virtual or face‑to‑face), 1.5 hours