You will be an employee of Mercari Japan and working in our Tokyo office, primarily for the US version of Mercari. As a Data Engineer at Mercari, you will be able to apply your data infrastructure and platform knowledge to improve Mercari’s online consumer-to-consumer marketplace and develop new innovative features.
What you will be doing:
- Design, build and maintain ETL pipelines for data analysis and production use
- Operate and optimize our data analysis system
- Combine data collected from a variety of sources to accelerate machine learning efforts
- Collaborate with teams of data analysts, product managers and engineers in Japan and the U.S. to accelerate data-driven business growth
- Increasing productivity organization-wide by standardizing data engineering tools and processes
- Along with our service growth, the complexity and amount of data are increasing. It is time to make a major overhaul of our current data pipelines, processes and analytics database structure.
- Providing a better data analysis environment to data analysts and product managers will enable more efficient data-driven decision making and advanced analytics. Additionally, it also allows us to build clean datasets quickly, so we can use ML/AI technologies to provide fascinating and useful features for our customers.
- We are looking forward to applications from engineers who enjoy finding problems on their own and coming up with solutions, as well as getting your hands dirty with ongoing issues and projects.
- Over 5 years of experience in software engineering, and at least 3 years in developing applications in Python, PHP or Golang
- Experience working on end-to-end development of backend systems
- Advanced knowledge of databases, real-time and batch data pipelines, SQL and data analysis
- Fundamental knowledge and troubleshooting skills in security, Linux, logging, and system operations
- Good communication and interpersonal skills, with the ability to collaborate across teams
- Ability to develop features to improve system performance and reliability
- Experience with data pipeline and workflow management tools (e.g., Airflow, Azkaban, Luigi)
- Experience using cloud services (e.g., AWS, GCP, Microsoft Azure)
- Experience using BI/data visualization tools (e.g., Looker, Chartio, Tableau)
- Experience with large-scale distributed systems (e.g., Kubernetes, Hadoop, Spark)
- Experience with systems using containerization technologies such as Docker and Kubernetes
- Fundamental knowledge of machine learning and AI