Senior Data Analystシニアデータアナリスト (Japan)

📋 Description

• • Serve as a key member of the Data Engineering team, owning data assets and collaborating with Data Pipeline Engineers and stakeholders in Live Operations and Marketing to build tools, analytics, and dashboards that drive critical business decisions.
• • Work with high-volume, real-time data ingested from multiple sources to enable actionable insights across the organization, including user acquisition, monetization, and finance functions.
• • Design, develop, and maintain scalable data pipelines using industry-standard technologies including Kafka, Imply Druid, EMR, Trino, and Airflow.
• • Build and optimize dashboards and analytical tools using Superset to support data-driven decision-making for cross-functional teams.
• • Apply SQL and SQL-like languages to query, transform, and analyze large datasets across distributed data systems.
• • Write well-tested, production-grade code using Python, Scala, or R, with rigorous adherence to unit testing, integration testing, and end-to-end testing practices.
• • Partner with engineering and operations teams to ensure data infrastructure supports real-time analytics and scalable reporting needs.
• • Contribute to the evolution of the company’s data architecture by identifying inefficiencies and implementing improvements in data ingestion, storage, and processing workflows.
• • Translate complex business requirements into technical solutions that enhance data accessibility, accuracy, and timeliness for non-technical stakeholders.
• • Maintain documentation for data pipelines, dashboards, and analytical models to ensure knowledge sharing and operational continuity.
• • Stay current with emerging data engineering trends and evaluate new tools or frameworks that can improve scalability, reliability, or performance of the data stack.
• • Participate in code reviews, sprint planning, and agile development cycles to ensure alignment with team goals and engineering best practices.
• • Support the integration of data systems with machine learning models and analytics platforms to enable predictive and prescriptive analytics capabilities.
• • Ensure data quality and consistency across systems by implementing validation checks and monitoring mechanisms for data pipelines.
• • Communicate technical findings and business insights clearly to both technical and non-technical audiences across global teams.