About the job
# About the Team
- The Data Mart Platform Team is dedicated to building a standardized Data Warehouse for various Toss products, aiming to prevent data silos and enhance overall data maturity across the organization.
- Responsibilities include enhancing centralized DW quality management processes, standard monitoring, integrating product data with the enterprise data mart, designing efficient pipelines, and creating standardized marts.
- **Interested in learning more about Toss's Data Organization?** [→ *Toss Data Division Wiki*](https://recruit-data-division.oopy.io/)
# Responsibilities
- After completing an onboarding process to familiarize yourself with Toss's DW standards, you will work as part of the Data Mart Platform Team.
- Maintain and manage an agile and manageable enterprise DW standard, taking responsibility for DW quality management from an enterprise perspective in collaboration with DAEs (Domain Analytics Engineers) from various product domains (development and execution of standard management monitoring).
- Plan and execute systems and processes to enhance data reliability, improving table consistency, advancing DQ rules, and establishing health check metrics.
- Develop enterprise-level marts, managing the integration of standard marts from different domains and ensuring efficient data pipeline improvements.
- Identify and execute tasks to enhance data discoverability across the organization.
- Develop a platform to measure data maturity across various Toss domains and initiate projects to enhance the productivity of DAEs.
- The data development environment is based on Hadoop, Airflow, Python, and SQL (Impala).
# Desired Qualifications
- Understanding of database normalization and the fundamental characteristics of Data Warehouses (Subject-Oriented, Integrated, Non-Volatile, Time-Variant).
- Ability to clearly define key concepts as a DW data modeler and propose efficient data structures based on diverse data perspectives.
- High-level understanding of DW standard management and the capability to propose and lead improvement initiatives at the enterprise level.
- Strong comprehension of data governance aspects, including data quality and compliance, with the ability to suggest actionable plans.
- Proficient in SQL, capable of writing efficient and readable queries.
- Basic Python skills (enough to work with Airflow) are acceptable, but understanding modules and PySpark code written by others is preferred.
- Experience with large-scale data processing and designing metrics from an AARRR perspective is a plus.
# Application Tips
- Please specify any relevant experience with DW construction projects and mart design, detailing your contributions.
- Mention specific challenges you have addressed regarding data maturity.
- Outline your contributions and lessons learned while solving data-related issues.
# Joining Toss
- Application Submission > Job Interview > Cultural Fit Interview > Reference Check > Compensation Discussion > Final Acceptance and Onboarding
# A Note to Future Colleagues
> "Our team strives for better service every day."
- I was drawn to the thrilling risks associated with financial data and saw that my growth could contribute to the company's success, which is why I joined Toss.
- The most stressful aspect of my previous company was being led by predetermined objectives, but Toss offers more autonomy than I expected, along with a dedicated and ambitious team focusing on "better service every day."
