Data Analytics Engineer - Mandarin Speaker (Fintech Company)

Data Analytics Engineer - Mandarin Speaker (Fintech Company)

About the role

We are looking for a Data Analytics Engineer to support data processing, regulatory reporting, automation, and business analytics. You will develop SQL and Python-based ETL processes, prepare regulatory reports, automate recurring tasks, and provide reliable data and analytical insights to support business decision-making.

You will work closely with Finance, Compliance, Product, and Operation teams in a fast-paced fintech environment.

Key responsibilities

• Develop and optimize SQL queries for data extraction, transformation, and reporting.
• Build and maintain Python scripts for ETL, automation, and data validation.
• Prepare, validate, and automate regulatory reports (SLIK, PUSDAFIL, FDC, APOLO, etc.).
• Provide accurate datasets and analytical reports for internal stakeholders.
• Analyze data to identify trends, anomalies, and business insights.
• Develop and maintain automation scripts and scheduled jobs (Cron or similar).
• Support machine learning initiatives by preparing datasets and maintaining data dictionaries and documentation.
• Collaborate with developers to understand APIs, backend systems, and application data flows.
• Ensure data accuracy, consistency, and proper documentation.

What we're looking for

• Bachelor's degree in Computer Science, Information Systems, Statistics, Engineering, or a related field.
• 2+ years of experience in Data Analytics, Business Intelligence, Data Engineering, or a similar role.
• Proficiency in Python for ETL and automation.
• Good understanding of relational databases and Linux.
• Strong analytical and problem-solving skills.
• Good written and spoken Mandarin.
• Able to work independently in a fast-paced environment.

Preferred

• Experience in fintech, banking, or other regulated industries.
• Experience with regulatory reporting.
• Familiarity with REST APIs, JSON, Git, and Cron jobs.
• Basic understanding of Android/iOS application architecture.
• Knowledge of machine learning concepts (e.g., regression, classification) and data preparation for model training.