Senior Systems Engineer, Data Management
Our field sales professionals rely on proactive technical support during the sales process – and our expert Systems Engineering team always steps up to the mark. We lead the development and implementation of complex and specialized products, applications, services and solutions. From delivering sales presentations and product demonstrations, to developing detailed installation or system integration plans, we ensure customers get the innovative, relevant, interoperable solutions they need.
Join us to do the best work of your career and make a profound social impact as a Senior Systems Engineer on our Systems Engineering Team in Tokyo.
What you’ll achieve
As a Senior Systems Engineer, you will provide pre-sales technical support to our field sales teams, helping to define the overall Dell Technologies solution for our customers using the full range of company products and services.
SUMMARY
Provides pre-sales technical support to field sales teams during the sales process. Responsible for ensuring the technical validity and interoperability of the solution, as well as aligning it directly with the customers' strategic business plans.
ACCOUNTABILITIES
- Provides technical expertise to sales organization in selecting, implementing, and developing competitive product and services applications and solutions
- Delivers technical presentations to showcase product capabilities and applications to technical users/buyers
- Prepares detailed product specifications for the purpose of selling high-end product and solutions
- Provides project scoping and co-ordinates internal specialists and inter-department activities
- Assists sellers in creating demand for product
You will:
•Build and lead relationships for highly sophisticated customer accounts
•Conduct customer needs analysis and anticipate requirements beyond existing solution’s scope
•Prepare detailed product specifications to enable the sale of our products and solutions, and deliver impact presentations at customer facilities
•Verify operability of sophisticated product and service configurations within the customer’s environment
•Perform advanced systems integration and provide technical expertise to design and implement the solution
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).
