Note: The job is a remote job and is open to candidates in USA. Dell Technologies is hiring an Associate Systems Engineer, Data Management to provide pre-sales technical support to field sales teams. The role helps define customer solutions, analyzes customer needs, presents and demonstrates products, verifies configurations, and designs and implements integrated solutions.
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
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)
- 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)
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