RESPONSIBILITIES:
- Lead the development and deployment of Woven City's data architecture
- Collaborate with cross-functional teams, including product managers, data scientists, and engineers, to translate high-level goals into actionable data solutions
- Design and architect efficient, scalable systems for data collection, storage, processing, and analytics that cater to Woven City's needs
- To maintain high standards across the team, implement best practices for data modeling, data quality, testing, and documentation
- Guide and mentor data engineers, fostering technical growth and best practices within the team
- Identify potential technical challenges, mitigate risks, and provide technical support to other teams regarding data architecture
- Conduct code reviews and data architecture reviews and provide guidance on improving system performance, data lifecycle management and data security
- Work closely with teams across the organization to ensure seamless integration with data sources, APIs, and third-party data tools
MINIMUM QUALIFICATIONS:
- 8+ years of experience in data architecture and data engineering, with a focus on complex, data-intensive applications
- Strong experience with data modeling, database design, and data warehousing solutions
- Expertise in building scalable data architectures and working with big data technologies (e.g., Hadoop, Spark)
- Proficiency with relational and NoSQL databases (e.g., PostgreSQL, MongoDB, Cassandra)
- Experience with cloud platforms (preferably AWS) and modern DevOps practices (CI/CD pipelines, Infrastructure as Code using Terraform)
- Solid understanding of data security, data governance, performance optimization, and scalability in cloud environments
- Proven track record of designing and implementing complex data platforms solving tangible business use cases
- Excellent communication skills and fluency in English and Japanese, with a proactive approach to problem-solving
NICE TO HAVES:
- Experience building data platforms for smart cities or large-scale IoT environments
- Experience with data streaming technologies such as Kafka or Kinesis
- Hands-on experience with data processing frameworks/platforms (e.g., Spark, Flink, Databricks)
- Experience with statistical methods and data analysis tools used in large-scale environments
- Experience in traditional business intelligence, coming up with or understanding strategies around structured dashboarding
- Some experience with LLM prompt-engineering, and the langchain ecosystem (e.g., PineCone, AutoGPT, PromptLayer, LlamaIndex)
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