Senior Systems Engineer – AI Data Platform

Language Skills Japanese: Native or professional level (JLPT1) English: Reading & Writing - Business Level / Speaking - Lower Business Level

Technical Skills Hands‑on experience with at least one major cloud data platform (e.g., Snowflake, Databricks, BigQuery, Redshift, Cloudera, Synapse, or similar). Strong understanding of data warehousing, data lakes/lakehouse, and ETL/ELT concepts (staging, modeling, performance tuning, cost/perf tradeoffs). Data engineering and integration including unstructured data processing (PDFs, logs, images, text) and transformation into structured/vectorized formats Strong SQL skills for analytical queries, performance tuning, and data modeling (star/snowflake schemas, dimensional modeling, partitioning, clustering). Unstructured data & AI/RAG: Understanding of vector databases (e.g., Elasticsearch, Milvus, pgvector), embedding models, and RAG architectures. Familiarity with document processing pipelines, chunking strategies, and semantic search patterns. Familiarity with data pipeline and orchestration tools (e.g., Airflow, dbt, Spark, Kafka, cloud-native ETL tools) and batch vs. streaming patterns. Understanding of data governance (catalog, lineage, security, RBAC, masking, compliance requirements like GDPR/CCPA). Analytics, BI, and data science Ability to design and explain analytics solutions end‑to‑end: from raw data to dashboards and predictive models. Working knowledge of BI tools (e.g., Tableau, Power BI, Looker, Qlik) and how to connect, model, and optimize for self-service analytics. Familiarity with data science and ML workflows (feature engineering, experimentation, model training/deployment, RAG pipeline development, prompt engineering) and tools/languages such as Python, Spark, notebooks, and ML frameworks (e.g., scikit‑learn, MLflow, TensorFlow/PyTorch, LangChain, LlamaIndex at a conceptual level).

Consulting Skills Skilled at asking the right questions to uncover technical requirements, constraints, and business drivers. Can translate ambiguous business problems into clear data and analytics use cases. Storytelling & communication Excellent at translating complex technical topics into clear, business‑oriented narratives for both technical and non‑technical audiences. Comfortable presenting to large groups and senior stakeholders (CIO/CDO, Heads of Data/Analytics). Demo & POC excellence Able to build and deliver compelling demonstrations that tell a story around customer data and use cases, not just features. Can structure and run POCs with clear success criteria, timelines, and executive readouts to accelerate technical win. Competitive positioning Understands the broader data & AI ecosystem and can articulate differentiation versus other data warehouses, data lake/lakehouse platforms, and analytics tools. Cloud data warehouse or lakehouse migrations Enterprise BI modernization/self‑service analytics GenAI and RAG implementations for enterprise knowledge management, intelligent document processing, or customer-facing AI applications Real‑time or streaming analytics Advanced analytics / data science enablement Hands‑on experience with at least one major public cloud (AWS, Azure, or GCP) and one or more leading data platforms (e.g., Snowflake, Databricks, Cloudera, BigQuery, Redshift, Synapse).

5+ years in a customer‑facing technical role such as Sales Engineer, Solutions Architect, Data Engineer, Analytics Consultant, or Data Scientist with strong commercial exposure.

Proven experience architecting and delivering data management, analytics, or data science solutions in one or more of the following areas: · Cloud data warehouse or lakehouse migrations · Enterprise BI modernization/self-service analytics · GenAI and RAG implementations for enterprise knowledge management, intelligent document processing, or customer-facing AI applications · Real-time or streaming analytics · Advanced analytics / data science enablement · Hands-on experience with at least one major public cloud (AWS, Azure, or GCP) and one or more leading data platforms (e.g., Snowflake, Databricks, Cloudera, BigQuery, Redshift, Synapse).