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:

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