Senior Data Engineer - MoneyLion

About the Role

The Kuala Lumpur office is the technology powerhouse of MoneyLion. We pride ourselves on innovative initiatives and thrive in a fast paced and challenging environment. Join our multicultural team of visionaries and industry rebels in disrupting the traditional finance industry!

At MoneyLion, we measure everything and rely on data to guide our decisions, including both long-term strategies and day-to-day operations.

As a Senior Data Engineer, your main goal is to support data scientists, analysts and software engineers by providing maintainable infrastructure and tooling they can use to deliver end-to-end solutions to business problems. You will work with terabytes to petabyte-scale data, in a complex data environment supporting multiple products and data stakeholders across the US and KL.

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 work with Redshift, Snowflake, EMR, Kubernetes, Airflow and more as the main tools of the job. You will write scalable and performant SQL queries running over billions of rows of data, and help simplify these processing to enable insights to be more easily extractable from them.

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.

Key Responsibilities

Design, implement, operate and improve the analytics platform

Design data solutions using various big data technologies and low latency architectures

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

Maintain the data warehouse with timely and quality data

Build and maintain data pipelines from internal databases and SaaS applications

Understand and implement data engineering best practices

Improve, manage, and teach standards for code maintainability and performance in code submitted and reviewed

Mentor and provide guidance to junior engineers on the job

About You

Expert at writing and optimising SQL queries

Proficiency in Python, Java or similar languages

Familiarity with data warehousing concepts

Experience in Airflow or other workflow orchestrators

Familiarity with basic principles of distributed computing

Experience with big data technologies like Spark, Delta Lake or others

Proven ability to innovate and leading delivery of a complex solution

Excellent verbal and written communication - proven ability to communicate with technical teams and summarise complex analyses in business terms

Ability to work with shifting deadlines in a fast-paced environment

Bonus Points

Authoritative in ETL optimisation, designing, coding, and tuning big data processes using Spark

Knowledge of big data architecture concepts like Lambda or Kappa

Experience with streaming workflows to process datasets at low latencies

Experience in managing data - ensuring data quality, tracking lineages, improving data discovery and consumption

Sound knowledge of distributed systems - able to optimise partitioning, distribution and MPP of high-level data structures

Experience in working with large databases, efficiently moving billions of rows, and complex data modelling

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:

Online Preliminary Codility test

Recruiter Screening Call

Take-home Assessment

Take Home Discussion (Virtual)

Interview - Hiring Manager (Virtual or face-to-face), 1.5 hours