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Data Virtualization Engineer Jobs in Austin, TX (NOW HIRING)

Arista Networks

POC Engineer

Austin, TX · On-site

Arista Networks is an industry leader in data-driven, client-to-cloud networking for large data ... in virtualized networks • Experience in routing & switching hardware platform tests • ...

Arista Networks

POC Engineer

Austin, TX · On-site

Company Description Arista Networks is an industry leader in data-driven, client-to-cloud ... Experience in network virtualization technologies such as VXLAN, OpenStack, etc. * Experience in ...

Arista Networks

POC Engineer

Austin, TX

Company Description Arista Networks is an industry leader in data-driven, client-to-cloud ... Experience in network virtualization technologies such as VXLAN, OpenStack, etc. * Experience in ...

Fluidstack

Controls Engineer, Virtualized PLCs

Austin, TX · On-site +1

$164K - $206K/yr

We acquire power, design and build data centers, and operate them - with teams spanning hardware ... You've worked with virtualization or software-based control runtimes. * You test control logic like ...

Fluidstack

Controls Engineer, Virtualized PLCs

Austin, TX · On-site

$82K - $107K/yr

We acquire power, design and build data centers, and operate them - with teams spanning hardware ... You've worked with virtualization or software-based control runtimes. * You test control logic like ...

AMD

Linux Kernel SW Engineer

Austin, TX · On-site

... virtualization, and security • Advanced Linux Kernel debugging skills across kernel subsystems ... data structures, x86-64 architecture, ACPI, and PCIE • Experience with low-level debug tools as ...

SpaceX

IT Storage Engineer

Bastrop, TX

Manage storage access, data permissions, and access controls across different protocols and ... Experience collaborating with virtualization teams as an infrastructure engineer. ADDITIONAL ...

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How much do data virtualization engineer jobs pay per hour?

As of Aug 16, 2026, the average hourly pay for data virtualization engineer in Austin, TX is $59.04, according to ZipRecruiter salary data. Most workers in this role earn between $48.85 and $67.45 per hour, depending on experience, location, and employer.

How does a data virtualization engineer typically collaborate with business analysts and data scientists?

Data Virtualization Engineers play a key role in bridging the gap between raw data and actionable insights by working closely with business analysts and data scientists. They design and maintain virtual data layers that allow these teams to access integrated, real-time data from multiple sources without having to manage the underlying complexities. Regular collaboration often includes requirements gathering, troubleshooting data access issues, and optimizing queries to ensure performance and accuracy. This cross-functional teamwork helps ensure that data-driven decisions are based on timely, trustworthy information.

What are the key skills and qualifications needed to thrive as a data virtualization engineer, and why are they important?

To thrive as a Data Virtualization Engineer, you need expertise in data integration, SQL, data modeling, and a solid understanding of database technologies, often supported by a degree in computer science or related fields. Familiarity with data virtualization tools such as Denodo, Cisco Data Virtualization, or IBM Cloud Pak for Data, as well as certifications in these platforms, are commonly required. Strong problem-solving skills, effective communication, and the ability to collaborate across business and IT teams are valuable soft skills. These competencies are essential for building efficient, scalable data solutions that enable organizations to access and manage data seamlessly from diverse sources.

What is a data virtualization engineer?

A Data Virtualization Engineer is an IT professional who specializes in creating and managing data virtualization solutions, which allow organizations to access and integrate data from multiple sources without physically moving or replicating it. They design, implement, and maintain data virtualization platforms, enabling real-time data access for analytics, reporting, and business intelligence. These engineers work closely with data architects, business analysts, and IT teams to ensure seamless data integration and optimal performance. Their work simplifies data management and helps organizations make faster, data-driven decisions.

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Sr. Consultant Machine Learning & Knowledge Graph Engineer

Dell Technologies

Round Rock, TX • On-site

$97K - $133K/yr

Other

This job post has expired today. Applications are no longer accepted.

Job description


Sr. Consultant Machine Learning & Knowledge Graph Engineer
Data Science is all about breaking new ground to enable businesses to answer their most urgent questions. Pioneering massively parallel data-intensive analytic processing, our mission is to develop a whole new approach to generating meaning and value from petabyte-scale data sets and shape brand new methodologies, tools, statistical methods and models. What's more, we are in collaboration with leading academics, industry experts and highly skilled engineers to equip our customers to generate sophisticated new insights from the biggest of big data.
Join us to do the best work of your career and make a profound impact as Sr. Consultant Machine Learning & Knowledge Graph Engineer on our growing and dynamic team in Round Rock, Texas.
What you'll achieve
Lead the architecture, development, and deployment of enterprise scale ML solutions across Dell's global ecosystem. Drive MLOps standards, build production grade ML services, and collaborate across engineering, product, and platform teams to enable AI at scale. Scale ML solutions across Dell's global ecosystem. As a Sr. Consultant Machine Learning & Knowledge Graph Engineer, you will play a pivotal role in advancing our AI and ML capabilities and creating Enterprise wide KG marketplace and Ontology layouts. This is a high-impact, enterprise-level technical leadership position responsible for defining and executing Dell's graph data strategy. You will architect production-grade Knowledge Graph platforms, design semantic data layers that power Agentic AI, and drive the convergence of graph technologies with large-scale data engineering ecosystems. This role demands a rare combination of deep graph expertise, distributed systems mastery, and strategic business influence.
You will
  • Knowledge Graph Architecture and Delivery: Design, build, and scale enterprise Knowledge Graph platforms using Neo4j and/or Stardog, establishing graph-native data models that enable entity resolution, relationship discovery, and semantic reasoning across business domains. Ontology and Semantic Layer Engineering: Define and govern enterprise ontologies (OWL 2), taxonomies, and semantic schemas that provide a unified, machine-interpretable view of Dell's data assets, ensuring consistency, reusability, and inferencing capability

  • Graph-Powered Agentic AI Infrastructure: Architect graph-backed Retrieval-Augmented Generation (RAG) systems, tool-calling interfaces, and dynamic prompt-to-graph query pipelines that fuel autonomous AI agent decision-making with deterministic, explainable knowledge. Data Virtualization and Federation: Lead the design of virtualized graph layers using Stardog Virtual Graphs or equivalent federation patterns, enabling real-time querying across SQL, NoSQL, and streaming data sources without mass ETL

  • Graph Data Science and Analytics: Operationalize advanced graph algorithms - community detection, centrality analysis, node embeddings (Node2Vec, FastRP), link prediction - using Neo4j GDS or equivalent libraries to extract actionable intelligence from connected data. Real-Time Graph Ingestion and Streaming: Design high-throughput, low-latency graph ingestion pipelines integrating Kafka, Spark Structured Streaming, and graph-native CDC mechanisms to maintain continuously updated knowledge representations

  • Enterprise Graph Governance: Establish comprehensive graph data governance frameworks including SHACL/SHEX constraint validation, RBAC-based graph security models, data lineage tracking, and ontology versioning strategies. Cross-Functional Strategic Partnership: Collaborate with Principal Data Scientists, AI/ML platform teams, product leaders, and executive stakeholders to identify high-value graph use cases and translate complex business problems into graph-solvable architectures

  • Technology Evaluation and Innovation: Continuously evaluate emerging graph technologies (GQL/ISO standards, vector-graph hybrid search, graph neural networks, LLM-to-graph interfaces) and provide executive-level recommendations on adoption. Mentorship and Engineering Culture: Serve as the technical anchor and mentor for Senior Advisors, Staff Engineers, and tech leads, cultivating deep graph expertise across the organization and driving a culture of engineering excellence and innovation

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:
  • Graph Architecture Mastery: Extensive hands-on experience designing and operating production-grade graph systems using Neo4j (Cypher, GDS, APOC, AuraDB, Causal Clustering) and/or Stardog (SPARQL, OWL 2 reasoning, Virtual Graphs, SHACL validation) along with Ontology and Semantic Modeling: Proven expertise in enterprise ontology engineering - OWL 2 profiles, RDF/RDFS, SKOS taxonomies, property graph modeling patterns, and schema evolution strategies at scale

  • Agentic AI and RAG Engineering: Deep practical understanding of building graph-backed data environments for autonomous AI agents, including knowledge retrieval pipelines, tool-calling orchestration, dynamic SPARQL/Cypher generation from natural language, and hybrid vector-graph search architectures

  • Distributed Systems and Data Scale: Expert-level command over PySpark, Kafka, data lakehouses (Apache Iceberg, Delta Lake), and enterprise orchestration (Airflow), with proven ability to integrate these with graph ecosystems and programming and query proficiency: Advanced fluency in Python, SQL, Cypher, and SPARQL, with strong software engineering practices (CI/CD, testing, version control, containerization)

  • Graph Data Science: Hands-on experience operationalizing graph algorithms - PageRank, Louvain, Label Propagation, node embedding techniques - and integrating graph-derived features into downstream ML/AI pipelines

  • Experience: 12+ years of progressive experience in data engineering, graph architecture, and cloud-native platform delivery, with at least 4+ years focused specifically on Knowledge Graph or semantic technology initiatives at enterprise scale

  • Strategic Leadership: Exceptional communication, advisory, and stakeholder-management skills, with a demonstrated history of driving large-scale technical transformations and influencing cross-functional technology strategy

Desirable Requirements
  • PhD or Master's degree in Technology, Computer Science, Machine Learning or equivalent quantitative field
  • Experience in data mesh or data fabric architectures with graph as the metadata backbone.

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