Function
Services
Our Company
We’re Hitachi Vantara, the data foundation trusted by the world’s innovators. Our resilient, high-performance data infrastructure means that customers – from banks to theme parks – can focus on achieving the incredible with data.
If you’ve seen the Las Vegas Sphere, you’ve seen just one example of how we empower businesses to automate, optimize, innovate – and wow their customers. Right now, we’re laying the foundation for our next wave of growth. We’re looking for people who love being part of a diverse, global team – and who get excited about making a real-world impact with data.
Job description
This role is open to candidates who are local and authorized to work in Toronto or New York.
What You Will be Doing
We are seeking a highly skilled Forward-Deployed Engineer (FDE) with deep expertise in data virtualization, hybrid cloud architectures, and AI-driven solution validation. This role partners directly with customers to design and prototype composable data and AI solutions that validate technical feasibility, performance assumptions, and measurable business value across distributed data ecosystems.
The FDE role blends hands-on engineering, limited-scope economic modeling, and customer-facing technical storytelling. The ideal candidate thrives in the field, integrating partner technologies, building working prototypes, and demonstrating GenAI and agentic AI outcomes using customer and industry datasets.
Role Boundaries & Ownership
This role focuses on proving technical feasibility and business value through hands-on prototyping and customer facing engagements.
This role is not:
A Professional Services delivery role responsible for production implementationA standard Pre-Sales or Solution Architecture role focused on product configurationAn operational or run-state ownership roleThis role partners closely with:
Sales and Solution Architecture for opportunity shaping and technical validationProfessional Services and Managed Services for delivery and operational handoffProduct and Engineering teams for field feedback, validation, and learning transferKey Responsibilities
Data Architecture & Engineering
Design and prototype data virtualization architectures enabling unified, near real-time access to distributed data sources for validating feasibility, performance, and integration assumptions.Build prototype-level API-based extraction, streaming, ETL, and ELT pipelines.Integrate distributed query and transformation engines such as Spark, Presto, and SQL-based platforms in POV environments.Create reference architectures and working prototypes across AWS, Azure, GCP, private cloud, and on-premises environments.Perform workload and performance evaluations for compute, networking, storage, and GPU-accelerated environments.AI, GenAI & Agentic AI Enablement
Execute proof-of-value engagements using customer and industry datasets.Build AI-driven prototypes showcasing RAG, automation, and agentic workflows.Demonstrate AI feasibility using hybrid and GPU-accelerated environments.Translate prototype outcomes into technical and business value narratives.Financial & Efficiency Modeling
Build lightweight ROI, TCO, and cost comparison models to support POV outcomes.Evaluate architectural trade-offs within bounded validation efforts.Estimate financial impact of GenAI and agentic AI use cases.Customer Engagement & Presales
Translate business requirements into prototype-level architectures.Lead discovery sessions, technical workshops, and POV execution.Collaborate across sales, solution architecture, product, and delivery teams for handoff.What You Will Bring to the Team
Technical Skills & Experience
Strong experience with data virtualization platforms such as Zetaris, Starburst, Dremio, or equivalent technologies.Hands‑on exposure to modern data platforms including Databricks, Snowflake, Teradata, and cloud‑native data warehouses.Proficiency with Spark, Presto, SQL, distributed query engines, and performance optimization concepts.Experience building prototype‑level ETL/ELT pipelines and integration workflows.Knowledge of API‑based data integration patterns.Experience working in hybrid,…
