Senior Systems Engineer, Data Management

Dell Technologies•NC, NC

About The Position

The Senior Systems Engineer, Data Management provides pre-sales technical support to field sales teams, helping define customer solutions using the company’s products and services. The role is part of the Systems Engineering Team and supports the East Region of the United States. The engineer builds relationships with sophisticated customer accounts, analyzes customer needs, prepares product specifications and presentations, verifies that product and service configurations will operate in customer environments, and provides advanced systems integration expertise. The position focuses on data management, analytics, and data science solutions, including cloud data platforms, data warehousing and lakehouse technologies, data pipelines, BI, AI/RAG, and related customer demonstrations and proofs of concept.

Requirements

At least 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 areas: cloud data warehouse or lakehouse migrations; enterprise BI modernization or self-service analytics; GenAI and RAG implementations; real-time or streaming analytics; or advanced analytics and data science enablement.
Hands-on experience with at least one major cloud data platform, such as Snowflake, Databricks, BigQuery, Redshift, Cloudera, or Synapse, or similar.
Strong understanding of data warehousing, data lakes/lakehouse, and ETL/ELT concepts, including staging, modeling, performance tuning, and cost/performance tradeoffs.
Experience with data engineering and integration, including processing unstructured data and transforming it into structured or vectorized formats.
Strong SQL skills for analytical queries, performance tuning, and data modeling, including dimensional modeling, partitioning, and clustering.
Understanding of vector databases, embedding models, RAG architectures, document processing pipelines, chunking strategies, and semantic search patterns.
Familiarity with data pipeline and orchestration tools such as Airflow, dbt, Spark, Kafka, or cloud-native ETL tools, and with batch and streaming patterns.
Understanding of data governance, including catalogs, lineage, security, role-based access control, masking, and compliance requirements such as GDPR/CCPA.
Ability to design and explain end-to-end analytics solutions from raw data to dashboards and predictive models.
Working knowledge of BI tools such as Tableau, Power BI, Looker, or Qlik, including connecting, modeling, and optimizing for self-service analytics.
Familiarity with data science and machine learning workflows, including feature engineering, experimentation, model training and deployment, RAG pipeline development, and prompt engineering.
Familiarity with tools and languages such as Python, Spark, notebooks, and ML frameworks including scikit-learn, MLflow, TensorFlow/PyTorch, LangChain, or LlamaIndex at a conceptual level.
Ability to uncover customer technical requirements and business drivers, and translate complex technical topics for varied audiences.
Comfort presenting to large groups and senior stakeholders, including CIOs, CDOs, and heads of data or analytics.
Ability to create compelling demonstrations and run proofs of concept with clear success criteria, timelines, and executive readouts.
Understanding of the broader data and AI ecosystem and ability to articulate competitive differentiation.
Hands-on experience with at least one major public cloud (AWS, Azure, or GCP) and one or more leading data platforms such as Snowflake, Databricks, Cloudera, BigQuery, Redshift, or Synapse.

Responsibilities

Build and lead relationships for highly sophisticated customer accounts.
Conduct customer needs analysis and anticipate requirements beyond the scope of existing solutions.
Prepare detailed product specifications to support sales of products and solutions.
Deliver presentations at customer facilities.
Verify operability of sophisticated product and service configurations in customer environments.
Perform advanced systems integration and provide technical expertise to design and implement solutions.
Provide pre-sales technical support to field sales teams and help define customer solutions using company products and services.
Ask questions to uncover technical requirements, constraints, and business drivers; translate ambiguous business problems into data and analytics use cases.
Present complex technical topics as clear, business-oriented narratives to technical and non-technical audiences, including senior stakeholders.
Build and deliver customer-focused demonstrations and structure and run proofs of concept with success criteria, timelines, and executive readouts.
Explain competitive differentiation across data warehouses, lakehouse platforms, and analytics tools.