시니어 데이터 엔지니어 (Senior Data Engineer)

About Us

FairSquare Lab is a digital asset infrastructure company that brings traditional finance on-chain. We have built digital asset infrastructure for domestic Tier-1 and Tier-2 financial institutions and are now expanding our stage globally.

• We are leading Project Pangea, a global payment and settlement infrastructure project with financial institutions from Korea and Europe.
• It is rare for domestic companies to lead global financial infrastructure projects. We are at the center of that effort.

Kloint is a digital asset intelligence and compliance subsidiary of FairSquare Lab. We analyze blockchain data to help financial institutions and enterprises use digital assets more safely and transparently.

• We provide virtual asset Anti-Money Laundering (AML) and Know Your Transaction (KYT) transaction monitoring solutions to support compliance for financial institutions and virtual asset operators.
• Based on blockchain forensics and digital asset tracking technology, we provide services for illegal transaction detection, risk analysis, and asset flow tracking.
• We are building core infrastructure that ensures trust and safety in an era where digital assets are spreading to the regulated financial sector.

The Role

As a Data Engineer, you will work on designing and operating batch and near-real-time ETL/ELT pipelines that power our compliance and intelligence platform.

Responsibilities

• Design and operate batch and near-real-time ETL/ELT pipelines that collect, ingest, and transform service logs and operational data
• Design and improve data lake/warehouse architecture based on the Hadoop ecosystem (HDFS, Hive, Spark, etc.)
• Build scheduling, monitoring, and incident response systems using workflow orchestration tools like Airflow
• Establish data quality and integrity validation systems, and manage metadata and data catalogs
• Provide clean datasets that analysts and data scientists can use immediately, and support ML training and serving pipelines
• Optimize pipeline performance and resource utilization to reduce operational costs
• Define data ingestion and utilization standards, and lead technical guides and code reviews within the team

Requirements

• 5-10 years of experience in data engineering or backend/data development
• Proficiency in writing data processing code with Python, Java, or Scala
• Strong SQL skills with experience in large-scale data modeling and query optimization
• Experience processing large-scale data using the Hadoop ecosystem
• Hands-on experience operating pipelines with tools like Airflow
• Experience diagnosing and stabilizing production pipeline failures
• Familiarity with Linux environments and Git-based collaboration

Nice to Have

• Experience working with blockchain on-chain data (transaction, block, and event log collection; node/RPC integration; on-chain data indexing and normalization)
• Experience directly operating, scaling, and tuning on-premises Hadoop clusters
• Experience with streaming and near-real-time processing using Kafka or similar tools
• Experience with dbt, data catalogs, and data quality monitoring tools
• Experience building ML pipelines (feature stores, model training/serving data)
• Experience applying data governance and security policies (PII anonymization, access control)
• Experience mentoring junior engineers or leading small teams
• Experience operating containers and Infrastructure-as-Code (Docker, Kubernetes, Ansible, etc.)

Ideal Team Member

• Someone who takes data reliability as their personal responsibility
• Someone who digs deep into root causes and solves problems fundamentally
• Someone who actively communicates and collaborates with other teams such as analytics and services
• Someone who continuously thinks about better architecture and automation