Senior Systems Engineer, Data Management

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).