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Senior Systems Engineer, UDS Data Management - East

Dell

Astoria, NY 11102

Remote

Proven experience architecting and delivering data management, analytics, or data science solutions in one or more of the following areas:
Ability to design and explain analytics solutions end-to-end: from raw data to dashboards and predictive models
Skilled at asking the right questions to uncover technical requirements, constraints, and business drivers
Competitive positioning
Can structure and run POCs with clear success criteria, timelines, and executive readouts to accelerate technical win
Strong understanding of data warehousing, data lakes/lakehouse, and ETL/ELT concepts (staging, modeling, performance tuning, cost/perf tradeoffs)
Comfortable presenting to large groups and senior stakeholders (CIO/CDO, Heads of Data/Analytics)
Enterprise BI modernization/self-service analytics
Advanced analytics / data science enablement
Unstructured data & AI/RAG: Understanding of vector databases (e.g., Elasticsearch, Milvus, pgvector), embedding models, and RAG architectures
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
Strong SQL skills for analytical queries, performance tuning, and data modeling (star/snowflake schemas, dimensional modeling, partitioning, clustering)
Excellent at translating complex technical topics into clear, business-oriented narratives for both technical and non-technical audiences
Storytelling & communication
Cloud data warehouse or lakehouse migrations
Consulting Skills
Demo & POC excellence
GenAI and RAG implementations for enterprise knowledge management, intelligent document processing, or customer-facing AI applications
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)
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)
Can translate ambiguous business problems into clear data and analytics use cases
Data engineering and integration including unstructured data processing (PDFs, logs, images, text) and transformation into structured/vectorized formats
Working knowledge of BI tools (e.g., Tableau, Power BI, Looker, Qlik) and how to connect, model, and optimize for self-service analytics
Hands-on experience with at least one major cloud data platform (e.g., Snowflake, Databricks, BigQuery, Redshift, Cloudera, Synapse, or similar)
Analytics, BI, and data science
Real-time or streaming analytics
Familiarity with document processing pipelines, chunking strategies, and semantic search patterns
Understands the broader data & AI ecosystem and can articulate differentiation versus other data warehouses, data lake/lakehouse platforms, and analytics tools
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)
Able to build and deliver compelling demonstrations that tell a story around customer data and use cases, not just features