Job detail for Analytics Engineer, Data Enabling

Eureka is the company behind "Pairs," an online dating app for people who are serious about finding a partner. Pairs is one of Japan's largest online dating apps, with over 27 million cumulative registrations in Japan. Since launching the Japanese version in 2012 and the Taiwanese version in 2013, the service has grown steadily, and over 900,000 people have found a partner through Pairs. In 2015, we joined Match Group, which operates online dating apps worldwide. To realize our mission of "creating things that people will be glad they had in their lives," we are aiming for further growth not only in Japan but globally.

Job Summary: We are looking for an Analytics Engineer to join the Data Enabling team at Eureka, the company supporting the growth of the online dating app "Pairs." In this position, you will work to provide data in an efficient and reliable manner by reflecting the needs of business and analytics teams through data modeling and data pipeline construction, ensuring an environment where data is consistently and correctly utilized within our expanding product and organization. As a data administrator, you will have ownership of product data. This is a key position expected to support data utilization across the entire organization and strengthen the foundation for analysis and decision-making by defining usage guidelines and data quality standards, maintaining data catalogs, managing access, and conducting quality tests. Furthermore, in the rapidly evolving AI era, you will lead the realization of an "AI-Native data utilization environment" where anyone can correctly acquire and analyze data using natural language without specialized analytical skills, accelerating the speed of decision-making. You will drive the construction of a semantic layer, ontology, and Business Context that allows not only humans but also LLMs and AI agents to accurately interpret and query data without , thereby leading the enabling of data analysis.

What You’ll Do:

• Interviewing stakeholders to understand business and analytical needs, and designing data models that can be accurately understood and queried by both humans and AI Agents.
• Designing, implementing, and operating a Semantic Layer that centralizes metric definitions and calculation logic to ensure AI Agents remain accurate.
• Developing an ontology and Business Context foundation that structures the relationships between business concepts and metrics.
• Implementing and operating layered data pipelines using Dagster and dbt based on designed data models.
• Defining data quality based on DMBOK and promoting test design, automation, and Data SRE-style quality monitoring and SLI/SLO operations to maintain quality.
• Building feedback loops for monitoring, evaluating, and continuously improving SQL generation and response accuracy by AI Agents.
• Managing metadata and documentation using data catalogs.
• Designing and operating data usage guidelines and access controls, and handling data-related monitoring.
• Enabling non-engineers to autonomously utilize data on a daily basis using AI Agents.

Basic Qualifications:

• Knowledge and experience in understanding business and analytical use cases and designing and implementing appropriate data models.
• Experience contributing to business decision-making and improvement through data analysis and aggregation using SQL.
• Experience in query performance optimization and cost/scan volume reduction based on an understanding of cloud DWH characteristics.
• Practical experience in building, testing, and operating data pipelines using data transformation frameworks such as dbt.
• Experience operating data pipelines using job orchestrators (e.g., Dagster, Airflow).
• Knowledge and practical experience in defining data quality and designing tests and monitoring to maintain that quality.
• 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 / GCP.
• Ability to proactively communicate across multiple teams (Engineering, PdM, BI, Business) to organize issues and reach consensus.
• Japanese: Business level or higher.

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 doing so.
• Someone with strong ownership in the data domain who can involve the entire company in raising issues and proposing enabling solutions.
• Someone with a passion for proactively catching up on the latest trends related to AI Agents, semantic layers, and ontologies, and pursuing their application to our products and infrastructure.
• Someone who can hold an ideal "To-Be" vision while engaging in discussions to break it down into real-world problems, bridge the gaps, and implement solutions in stages.