Company Overview
We plan and operate "Pairs", a dating and marriage matchmaking app for people who are serious about finding a partner. The service launched in Japan in 2012, a Taiwan version followed in 2013, and a Korean version was released in 2025. Cumulative registrations exceed 27 million (as of April 2026), and many people use it. In addition, more than 900,000 people have met irreplaceable partners through Pairs.
Job Summary:
We are hiring a Data Product Engineer to join the Data Enabling team at Eureka, Inc., which supports the growth of the online dating app "Pairs". In this role, amid a continuously expanding product and organization, you will work to create an environment where data is consistently and correctly utilized. By designing data models and building data pipelines that reflect the needs of business and analytics teams, you will deliver data efficiently and reliably. As the data steward owning product data, you will define usage guidelines and data quality standards, maintain the data catalog, manage access, and run quality tests. This supports data utilization across the organization and strengthens the foundation for analysis and decision-making. This is an important position. Furthermore, in the rapidly evolving AI era, you will lead the realization of an "AI Native data utilization environment" where anyone can retrieve and analyze data correctly in natural language, even without specialized analytics skills, accelerating decision-making. You will drive the construction of semantic layers, ontologies, and Business Context so that humans as well as LLMs and AI agents can correctly interpret and query data without misunderstanding it, leading the enablement of data analysis.
What Makes This Role Exciting:
Ownership of data utilization: As the owner of product data, you will consistently take responsibility for data models, pipelines, quality, catalog, and access management. Rather than simply fulfilling requests, you will bring the ideal state of the foundation into reality yourself. Building the frontier of an AI Native data environment: You will arrange the Semantic Layer, ontologies, and Business Context so that both people and AI agents can handle data correctly. You will test and validate the latest initiatives using real product data and real users. Scale and impact: You will handle data at the scale of Pairs' user base. The metrics and foundation you build will have significant impact, contributing to company-wide decision-making. The joy of implementing step by step from the To-Be: As the product grows, the volume and usage of data keep expanding. You will envision the ideal, translate it into real-world challenges, and shape it one step at a time. There are also opportunities to collaborate with product and marketing teams, gaining insights beyond the technical organization.
What You'll Do:
Gather needs from business and analytics teams and design data models that both humans and AI agents can accurately understand and query. Design, implement, and operate a Semantic Layer that centrally manages metric definitions and calculation logic so AI agents do not get lost. Build the ontology and Business Context foundation that structures the relationships between business concepts and metrics. Implement and operate layered data pipelines using Dagster and dbt based on the designed data models. Define data quality based on DMBOK, design and automate tests to maintain quality, and promote Data SRE-style quality monitoring and SLI/SLO operations. Build a feedback loop for monitoring, evaluating, and continuously improving SQL generation and answer accuracy by AI agents. Manage metadata and maintain documentation using the data catalog. Design and operate data usage guidelines and access controls, and handle data-related monitoring and response. Enable non-engineers to autonomously utilize data daily using AI agents.
Basic Qualifications:
Knowledge and experience in understanding business and analytics use cases and designing and implementing appropriate data models. Experience contributing to business decisions and improvements through data analysis and aggregation using SQL. Experience optimizing query performance and reducing cost and scan volume, based on an understanding of cloud DWH characteristics. Practical experience building, testing, and operating data pipelines using data transformation frameworks such as dbt. Experience operating data pipelines using a job orchestrator such as Dagster or Airflow. Knowledge and hands-on experience defining data quality and designing and monitoring tests to protect it. Experience implementing data analysis, processing scripts, and jobs using Python. Basic knowledge of data architecture and data governance. Basic knowledge of cloud platforms such as AWS and GCP. The ability to proactively communicate across multiple teams (engineering, PdM, BI, business), organize issues, and build consensus. Japanese: business level or above.
Tools we use:
AWS: Aurora MySQL, DynamoDB, S3, SQS, Kinesis Stream, etc. Google Cloud: BigQuery, Cloud Storage, GCE, Vertex AI, etc. Orchestration & Transformation: Dagster, dbt, Fivetran BI & Analytics: Tableau, Mixpanel, Looker Studio, Google Sheets AI & LLM: OpenAI API, Anthropic Claude
Characteristics:
Someone who can proactively communicate across multiple teams and enjoys it. Someone with strong ownership in the data domain who can involve the whole company, raise issues, and propose enablement initiatives. Someone enthusiastic about proactively catching up on the latest technology trends relevant to the business, such as AI agents, semantic layers, and ontologies, and pursuing their application to products and the foundation. Someone who holds an ideal (To-Be) while bridging gaps through discussion and step-by-step implementation grounded in real-world problems.