Senior Systems Engineer, UDS Data Management - East

Dell Technologies is a leading company in the technology sector, and they are seeking a Senior Systems Engineer to provide pre-sales technical support. In this role, you will help define comprehensive solutions for customers, ensuring the implementation of sophisticated products and services.

Responsibilities:

• 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

Requirements:

• 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)
• 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
• 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)
• 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
• Understands the broader data & AI ecosystem and can articulate differentiation versus other data warehouses, data lake/lakehouse platforms, and analytics tools
• 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)