- Toss Insurance was established with the goal of innovating the 'insurance' sector among various financial areas Toss aims to innovate.
- The Toss Insurance Data/Infrastructure Team makes the data across the insurance domain reliable and consistent.
- Due to the nature of the insurance industry, we deal with contracts, commissions, customer behavior, and underwriting data, where data quality directly impacts customer trust and compliance.
- Insurance data is not just simple numbers; it is the basis for decision-making that reduces customer risk. Our team ensures that this data is always accurate and delivered quickly to colleagues.
1. Data Modeling & DW Construction
- Based on Toss Insurance DW standards, design and operate standard marts for responsibility domains (IA Sales, Contracts, Commissions, Customers).
- Aim for a Single Source of Truth by maintaining and managing the marts so that colleagues can make decisions based on the same data criteria.
- Design a structure that reflects the principles of normalized DB design and the characteristics of DW (Subject-Oriented, Integrated, Non-Volatile, Time-Variant).
- Clearly distinguish between standard area marts (Conformed Mart) and consumption area marts (Data Mart) in operation.
2. Data Quality Management (DQ)
- Build data consistency verification logic and automate DQ monitoring.
- Perform anomaly detection in data according to insurance domain characteristics (contract errors, commission discrepancies, duplicate contracts, etc.).
- Document and maintain the definitions, column descriptions, and calculation logic of the marts through a metadata management system.
3. Pipeline & Security
- Develop and operate batch pipelines based on Airflow.
- Perform personal data (PII) masking and access control through data security reviews.
- Support data outputs compliant with regulatory reporting and IFRS accounting standards due to the nature of the insurance industry.
4. Collaboration with Developers
- From the service development stage, review log design and data models with server developers and propose analysis requirements.
- Collaborate with Data Analysts, Sales Support Team, Operations Team, and Platform Team to define metrics and handle data requests.
- Must be proficient in SQL at an advanced level. We welcome those who can write complex aggregate queries, window functions, and subqueries clearly.
- Understanding of database normalization principles and data warehouse design patterns (Star Schema, Snowflake Schema) is required.
- As a data modeler, you should be able to clearly define domain concepts and design easy and clear data structures.
- You should have communication skills to convert business requests into data structures.
- Proactivity is needed to discover data quality issues and propose solutions.
Major Technical Stack
- Required: SQL (MySQL / Oracle), Apache Airflow, Git
- Preferred: Python, dbt, PySpark, Tableau, Snowflake
- It would be nice to have a beginner level or above in Python (capable of writing Airflow DAGs and understanding others' code).
- Experience in data transformation using dbt (Data Build Tool) would be good.
- Experience using distributed processing environments such as PySpark would be good.
- Experience using cloud data warehouses like Snowflake would be good.
- Experience in visualization/reporting using Tableau would be nice.
- It would be nice to have experience designing and operating everything from data collection/loading to analysis/visualization from A to Z.
- Understanding of the AARRR funnel analysis framework would be preferred.
- Insurance/financial domain knowledge (contracts, commissions, claims, risk) or related data work experience would be good.
- Experience with large-scale data processing (hundreds of millions of records or more) would be preferred.
- Understanding the characteristics of insurance data under IFRS 17 accounting standards would be preferred.
Application > Job Interview > Cultural Fit Interview > Compensation Negotiation > Reference Check > Final Offer
1. Please make sure to check the following:
- The workplace is on the 37th floor of D Cube City Tower, 662 Gyeongin-ro, Guro-gu, Seoul.
- Persons with disabilities and national veterans are given preference in accordance with relevant laws.
- If any false information is discovered in your resume and submitted documents or if disciplinary actions are confirmed during your work history, employment may be canceled.
- Those who are prohibited from hiring or have disqualifying factors according to Toss Insurance internal regulations may have their employment canceled.
2. We recommend writing your resume as follows:
- If you have experience in DW construction or data mart design, please detail the specific contributions you made.
- Please quantify the scale of the data you managed (number of rows, number of tables, processing cycles).
- Provide specific examples of data quality problems you discovered and solved.
- It would be good to describe your collaborative methods with other roles (developers, analysts, business teams).
- Please write about why you are applying for this position and the background that made you interested in the Toss Insurance domain.
- Instead of simply listing experiences, focus on impacts and learning points.
