T

Toss

Fintech super-app for payments and banking

Data Engineer – Financial Data/DRE

Data EngineeringConfirmed live in the last 24 hours
Full-timeSeoul, South KoreaNo salary listedIn-person

About the Job

Requirements

  • At least 3 years of experience in data engineering or data platform development and operations.
  • Ability to implement and test production-grade data validation and processing logic using Python and SQL.
  • Experience building and operating data pipelines based on Airflow and Kubernetes.
  • Experience designing and operating data consistency and quality validation logic using RDBMS and distributed query engines such as Impala, Spark, or ClickHouse.
  • Experience taking responsibility for data incidents from detection through recovery and recurrence prevention.
  • Ability to collaborate and communicate with people in different roles, including Product Owners, Software Engineers, and Machine Learning Engineers.
  • Ability to independently define requirements and problems and use AI tools to build and operate an entire platform, from validation engines to administrative interfaces.

Responsibilities

  • Understand the sources and ingestion paths for assigned data domains, including stock disclosures, earnings, market prices, and news, and design and operate the full pipeline from data intake through processing, validation, and delivery.
  • Define data quality standards and consistency validation systems for assigned domains as data governance rules; trace issues from source to customer-facing surfaces, resolve root causes, and prevent recurrence.
  • Develop and operate a data quality platform that defines quality standards using declarative data contracts and automatically validates and observes them.
  • Trace data lineage from customer-facing service surfaces back to sources, identify validation gaps, and build systems to cross-validate external vendor data against internal source data.
  • Improve observability for Airflow- and Kubernetes-based data pipelines and automate recurring operations, such as data incident collection and root-cause tracing, using AI agents.
  • Own the reliability of the assigned data domain throughout its data lifecycle.

Desired Qualifications

  • Experience introducing or operating data contract-based quality validation tools such as ODCS, dbt tests, Great Expectations, or Soda.
  • Experience designing or operating SRE practices such as SLI/SLOs, error budgets, and incident management systems.
  • Understanding of and experience with large-scale data platforms and distributed processing, including Iceberg, ClickHouse, or Elasticsearch.
  • Experience using AI coding tools such as Claude Code or Codex in design, implementation, and verification, and independently validating their output.
  • Strong interest in securities-domain products and personal investment experience or principles.

About the Company

Toss

Fintech super-app for payments and banking

Toss is a South Korean financial services platform that started as a peer-to-peer money transfer service and has grown into a digital 'super app' for personal finance. Its products are accessed through one mobile interface that unifies bank accounts, credit cards, loans, investments, and other financial services, including its own Toss Bank and Toss Securities. It differentiates itself by offering a broad, integrated ecosystem—combining digital banking, asset management, and brokerage services under one roof and running its own banking and securities units. Its goal is to become the leading consumer finance platform in South Korea while expanding its user base globally.

Company size
1,001-5,000
Company stage
Series G
Total funding
$1.5B
Headquarters
Seoul, South Korea
Founded
2011

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