Senior Systems Engineer, Data Management

Dell Technologies
North Carolina, NC

Who this role is best for

Geared toward candidates with hands-on data engineering experience and strong consulting abilities, comfortable with designing complex data solutions and delivering compelling customer demonstrations.

Best fit for

  • Candidates with 5+ years in customer-facing technical roles and proven solution delivery experience
    — “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.”
  • Individuals who can translate technical complexity into business narratives for leadership audiences
    — “Excellent at translating complex technical topics into clear, business-oriented narratives for both technical and non-technical audiences.”
  • Professionals with a background in cloud data migrations and analytics modernization
    — “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.”

Things to consider

  • The role requires a strong grasp of both structured and unstructured data workflows
    — “Data engineering and integration including unstructured data processing (PDFs, logs, images, text) and transformation into structured/vectorized formats”
  • Candidates must be prepared to engage in high-level technical discussions with C-suite stakeholders
    — “Comfortable presenting to large groups and senior stakeholders (CIO/CDO, Heads of Data/Analytics).”

How to stand out

  • Highlight end-to-end solution design experience in analytics and data governance frameworks
    — “Ability to design and explain analytics solutions end-to-end: from raw data to dashboards and predictive models.”
  • Demonstrate expertise in vector databases and RAG architectures in your portfolio
    — “Understanding of vector databases (e.g., Elasticsearch, Milvus, pgvector), embedding models, and RAG architectures.”
  • Showcase your ability to structure and execute POCs with measurable outcomes
    — “Can structure and run POCs with clear success criteria, timelines, and executive readouts to accelerate technical win.”
  • Emphasize your experience in cloud-native ETL tools and data orchestration frameworks
    — “Familiarity with data pipeline and orchestration tools (e.g., Airflow, dbt, Spark, Kafka, cloud-native ETL tools) and batch vs. streaming patterns.”
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • providing pre-sales technical support
  • building and leading relationships for highly sophisticated customer accounts
  • conducting customer needs analysis
  • preparing detailed product specifications
  • verifying operability of sophisticated product and service configurations
Typical background
hands-on experience with cloud data platformsstrong understanding of data warehousing and ETL/ELT conceptsdata engineering and integration experience

Skills & requirements

Required

Cloud Data PlatformData WarehousingData Lakes/lakehouseEtl/elt ConceptsSQLData Engineering And IntegrationData GovernanceData Pipeline And Orchestration ToolsBI ToolsData Science And ML Workflows

Preferred

Ai/ragVector DatabasesEmbedding ModelsRAG ArchitecturesDocument Processing PipelinesChunking StrategiesSemantic Search Patterns

Stack & domain

Cloud Data PlatformData WarehousingData Lakes/lakehouseEtl/eltSQLVector DatabasesData Pipeline And Orchestration ToolsData GovernanceBI ToolsData Science And ML WorkflowsPythonSparkNotebooksML FrameworksLeadershipCommunicationProblem-solvingTeamworkData ManagementCloud ComputingData EngineeringData ScienceBusiness Intelligence

About the role

Original posting from Dell Technologies

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 the East Region of the US.

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.

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

Take the first step towards your dream career

Every Dell Technologies team member brings something unique to the table. Here’s what we are looking for with this role:

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.

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).

Source: Dell Technologies careers

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