Senior Systems Engineer, Data Management
Germany, remote
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 Germany.
What you’ll achieve
In this role, you will operate at the intersection of technology, customer success, and business impact. You will design modern data and AI architectures, translate complex requirements into clear use cases, and shape compelling solutions that drive our customers’ data and AI transformation.
You will:
1. Build trusted relationships with customers and lead discovery to understand their goals, challenges, and technical needs.
2. Design and validate modern cloud architectures for data, analytics, and AI/GenAI/RAG solutions.
3. Create clear solution designs, proposals, and technical materials that guide customers through their Data & AI transformation.
4. Deliver impactful demos and PoCs that highlight value, feasibility, and business outcomes.
5. Communicate complex data and AI concepts in a simple, customer-friendly way to both technical and non-technical stakeholders.
6. Evaluate modern data and AI platforms and explain how our solutions stand out in the competitive landscape.
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:
Essential Requirements
Technical Skills
7. Hands-on experience with leading cloud & data platforms
(Snowflake, Databricks, BigQuery, Redshift, Synapse, Cloudera) plus at least one major cloud (AWS, Azure or GCP).
8. Strong data architecture & engineering foundation
Data warehousing, data lakes/lakehouse, SQL, ETL/ELT, data modeling (star/snowflake), performance tuning, governance, and compliance (GDPR/CCPA).
9. Experience with data pipelines & unstructured data
Integration, orchestration (Airflow, dbt, Spark, Kafka), batch/streaming, and processing of PDFs, logs, images, text into structured/vectorized formats.
10. Familiarity with AI/GenAI & RAG concepts
Vector databases (pgvector, Elasticsearch, Milvus), embeddings, document processing, semantic search, chunking, and RAG pipeline fundamentals.
11. Broad analytics & BI knowledge
BI tools (Power BI, Tableau, Looker, Qlik), self-service analytics enablement, and end‑to‑end solution design from raw data to dashboards and models.
Consulting Skills
12. Strong customer-facing communication & storytelling
Ability to explain complex topics clearly to technical/non‑technical audiences; delivering presentations to senior stakeholders (CIO/CDO).
13. Discovery, requirements analysis & POC excellence
Skilled at uncovering business drivers, structuring PoCs, defining success criteria, and demonstrating solutions that showcase customer value.
Proven Experience
14. 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.
15. Track record delivering modern Data & AI solutions
Experience in cloud DWH/lakehouse migrations, BI modernization, GenAI/RAG implementations, streaming analytics, or advanced analytics programs.
Desirable Requirements
16. Hands-on experience with ML/AI workflows & tools
Feature engineering, model development, notebooks, Python, Spark, MLflow, TensorFlow/PyTorch, LangChain, LlamaIndex.
17. Ability to translate ambiguous business problems
Into clear data, analytics, or AI use cases with measurable value.
18. Understanding of competitive Data & AI landscapes
Ability to articulate differentiation across platforms (data warehouses, lakehouses, analytics tools, AI ecosystems).