Senior Systems Engineer, UDS Data Management - Dubai

Senior Systems Engineer, Data Management

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).

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