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PermanentBanqiao, New Taipei
Sr. Data Engineer (Consumer Data)Financial information / consumer app, consumer business group, full-time, Banqiao, New Taipei
The consumer business group (e-invoices, expense tracking, and lifestyle products) of a well-known Taiwanese financial information and consumer app company is hiring a senior data engineer: build stable data pipelines from sources such as MongoDB, working in a hybrid environment that combines cloud and on-premises systems.
Job Requirements at a Glance
- Salary & compensation
- Annual salary approx. NT$1.5 millionAnnual salary approx. NT$1.5 million, based on experience
- Nature of the role
- Full-time
- Location
- Banqiao District, New TaipeiThe exact location will be shared at the interview
* The above terms are current plans; actual compensation and working conditions will be as confirmed by both parties after the interview.
Job description
Build the data foundation for consumer raw data
- Extract, clean, transform, and store data from source systems such as MongoDB, building a stable, traceable, and scalable raw data pipeline so downstream analytics and data applications can rely on trustworthy data.
Design and maintain hybrid data pipelines
- Plan data flows, compute and storage between Cloud and On-premise based on data volume, processing latency and use cases, so that large volumes of consumer data move and are processed reliably.
Build Data Pipelines and data architecture that can handle large volumes of data
- Design data processing flows, data models, and query structures for batch and varying latency needs, making sound technical trade-offs between performance, stability, cost, and maintainability.
Improve data quality and pipeline stability
- Build data validation, anomaly monitoring, error handling and observability mechanisms to quickly pinpoint the root cause of missing data, delays or pipeline failures, ensuring downstream users get accurate and stable data.
Define data requirements with analytics, product, and related teams
- Understand the data granularity, update frequency, and usage that analytics and business applications truly need, and turn those needs into practical Data Pipelines and data architecture, rather than simply moving data according to a spec.
Continuously improve data engineering architecture and development processes
- Identify performance and maintainability issues in existing pipelines, storage, and data processing, and progressively improve data processing efficiency, engineering quality, and automation.
Support engineering needs for near-real-time data applications
- Help design and build Streaming / Near Real-time Pipelines for use cases such as recommendations, ads, and push notifications, shortening the time it takes for data to go from Source to being usable by downstream applications.
Requirements
Required
- Must have actually built and operated data pipelines — not just data analysis, BI, or dashboards
- Python + SQL is a must
- Understanding of and hands-on experience with both SQL and NoSQL
- Pipeline / architecture experience with large data volumes (over 100 million records)
- Hands-on experience in cloud data engineering
- Experience using coding agents to assist software development
Preferred qualifications
- Hands-on experience with Kafka or other event streaming technologies, having built and maintained Streaming / Near Real-time Data Pipelines in on-premise or hybrid environments.
- Experience supporting time-sensitive data applications such as recommender systems, advertising, and CRM/push notifications, with an understanding of the full data flow from event generation and processing to downstream serving.
- Data engineering experience in e-commerce, retail, or consumer industries
Why this opportunity
Build the data foundation from scratch
The core mission is turning raw data into a usable data foundation — you think through everything from source to serving.
Moving to real-time data
Real-time needs such as recommendations, ads, and push notifications are growing, so streaming experience maps closely to the problems the team is about to tackle.
How to apply
01
Initial interview with A-Cai, our AI interviewer
02
Confidential initial conversation with a consultant
03
Interview scheduling and agreement on terms
Apply now
The online form takes about 2 minutes, and a Step1ne consultant will contact you as soon as possible.
Talk to A-Cai, our AI interviewer, now Or ask us directly on LINE →