Data Engineer

This data engineer leads cross-business data platform projects from architecture design and technology selection through implementation, release, and ongoing operations. The work covers both new data platform launches and improvements to existing platforms, with responsibility for building reusable systems that can support different business requirements, constraints, and delivery speeds.

The role designs architectures that balance business needs, scale, cost, data quality, and security. It also separates technical capabilities and selects technologies for new platforms where there is no established precedent. Responsibilities include designing and implementing data warehouses and data lakes centered on BigQuery, building batch and streaming pipelines, and setting up workflow orchestration.

The engineer designs and continuously improves ELT processes and data models using dbt and Dataform. The role establishes monitoring based on SLOs and SLAs, responds to incidents, and promotes postmortems. It also covers data quality, metadata management, data governance, security, cost optimization, standardization, CI/CD, code quality, and operational quality.

As the lead for assigned projects, the person defines requirements, prioritizes work, coordinates costs, and builds agreement among stakeholders. The role works closely with data scientists, machine learning engineers, software engineers in business divisions, and analysts to determine suitable solutions. Contributions to the team include code reviews, technology selection discussions, support for junior members' design and implementation, and leadership of technical-stack standardization and code-quality improvements at team and department level.

The named cloud services are Google Cloud, BigQuery, Cloud Composer, Pub/Sub, Cloud Storage, Cloud Run, and Cloud Batch. The data technologies include dbt, Dataform, Apache Airflow, and Spark. The business intelligence tools are Looker and LookML. The listed languages are Python and SQL. The CI/CD and development tools are GitHub Actions, Terraform, and Docker. AI tools named in the posting are GitHub Copilot, Gemini, Cursor, Claude Code, and Devin, subject to governance for each business and project.

The data platform team supports multiple domains, including games, sports, healthcare, and live streaming. The role collaborates with specialists in data science, machine learning, and analytics and may work across domains rather than being fixed to one business or technology stack.

The stated work locations are DeNA's Shibuya office in Tokyo, the Yokohama Arai Building office, or the Yokohama BASEGATE office. Remote work is permitted from the employee's home or another location approved by the company, within the company's defined workplaces.