Senior Solutions Engineer – Big Data & Data Infrastructure - Remote

Overview

This is a great opportunity to be part of one of the fastest-growing infrastructure companies in history, an organization that is in the center of the hurricane being created by the revolution in artificial intelligence.

"VAST's data management vision is the future of the market."- Forbes

VAST Data is the data platform company for the AI era. We are building the enterprise software infrastructure to capture, catalog, refine, enrich, and protect massive datasets and make them available for real-time data analysis and AI training and inference. Designed from the ground up to make AI simple to deploy and manage, VAST takes the cost and complexity out of deploying enterprise and AI infrastructure across data center, edge, and cloud.

Our success has been built through intense innovation, a customer-first mentality and a team of fearless VASTronauts who leverage their skills & experiences to make real market impact. This is an opportunity to be a key contributor at a pivotal time in our company’s growth and at a pivotal point in computing history.

We are seeking an experienced Solutions Data Engineer who possess both technical depth and strong interpersonal skills to partner with internal and external teams to develop scalable, flexible, and cutting-edge solutions. Solutions Engineers collaborate with operations and business development to help craft solutions to meet customer business problems.

A Solutions Engineer works to balance various aspects of the project, from safety to design. Additionally, a Solutions Engineer researches advanced technology regarding best practices in the field and seek to find cost-effective solutions.

In Short

• Build distributed data pipelines using technologies like Kafka, Spark (batch & streaming), Python, Trino, Airflow, and S3-compatible data lakes.
• Design, deploy, and troubleshoot hybrid cloud/on-prem environments using Terraform, Docker, Kubernetes, and CI/CD automation tools.
• Implement event-driven and serverless workflows with precise control over latency, throughput, and fault tolerance trade-offs.
• Create technical guides, architecture docs, and demo pipelines to support onboarding and evangelize best practices.
• Integrate data validation, observability tools, and governance into the pipeline lifecycle.
• Own end-to-end platform lifecycle: ingestion → transformation → storage → compute layer.
• Benchmark and tune storage backends and compute layers for throughput, latency, and scalability.
• Work cross-functionally with R&D to push performance limits across analytics workloads.
• Operate and debug object store–backed data lake infrastructure.

Requirements

• 2–4 years in software / solution or infrastructure engineering, focused on large-scale data pipelines.
• Proficiency in Trino, Spark, and solid knowledge of Apache Kafka.
• Coding background in Python; familiarity with Bash is a plus.
• Deep understanding of data storage architectures including SQL, NoSQL, and HDFS.
• Solid grasp of DevOps practices, including Docker and Kubernetes.
• Experience with distributed systems and event-driven architecture.
• Hands-on familiarity with benchmarking and performance profiling.
• Excellent communication skills for guiding customer conversations.