Virtasant

Engineer

• Lead migrations of enterprise Spark workloads from on-premise environments to AWS and GCP
• Assess Spark applications, clusters, configurations, dependencies, data flows, and resource utilization
• Determine migration approaches across rehost, replatform, refactor, modernize, or retire
• Modernize traditional cluster-based workloads for serverless Spark where appropriate
• Design and implement architectures using AWS EMR Serverless, S3, Glue, Lake Formation, GCP Dataproc Serverless, GCS, and BigQuery
• Refactor legacy PySpark/Scala/Spark SQL applications for cloud portability, scalability, and reliability
• Migrate workloads using Hadoop, HDFS, YARN, Hive, and on-premise Spark clusters
• Troubleshoot and optimize Spark workloads, including partitioning, shuffle behavior, joins, data skew, execution plans, executor configuration, serialization, and SQL execution
• Benchmark performance and optimize serverless workloads for performance, reliability, and cloud cost
• Build reusable migration tooling, automation, templates, and frameworks
• Implement CI/CD and Infrastructure as Code using tools such as Terraform
• Define testing, validation, cutover, rollback, observability, and production-readiness patterns
• Partner with Data Engineering, ML, Cloud Architecture, Platform Engineering, DevOps/SRE, Security, Governance, and FinOps teams
• Own the complete migration lifecycle: Discover, Assess, Design, Refactor, Migrate, Validate, Optimize, Operate

Senior Serverless Spark Migration Engineer

Backend Engineer

• Design, refactor, and write patches for extensive Golang codebases
• Read and understand large Golang codebases
• Write comprehensive tests using the standard Golang library, Ginkgo, and Gomega
• Apply Test Driven Development (TDD) practices
• Troubleshoot applications using logs and profiling techniques
• Deploy, scale, and maintain applications on Kubernetes platforms
• Manage Kubernetes internal mechanisms and resource quota systems
• Develop API services in Golang
• Develop web/HTTP microservices
• Work on high-stakes, high-visibility projects with leading enterprises
• Lead accounts, shape engagements, and grow the portfolio

Senior Compute Engineer, Go, Kube

Artificial Intelligence

• Partner with executive sponsors to turn ambiguous mandates into concrete, time-boxed deployments with defensible business cases
• Lead technical discovery and architecture reviews
• Surface integration complexity, infrastructure constraints, change-control realities and technical tradeoffs
• Establish technical credibility with customer engineering teams and scope engagements around deployable, verifiable outcomes
• Sequence multi-phase cloud and AI optimization deployment roadmaps
• Work alongside customer engineers on infrastructure and AI optimization
• Own architecture decisions, technical problem solving, guardrails, policy layers and integration into client pipelines and change processes
• Navigate ticketing, change templates, approval routing, security and compliance gates
• Diagnose and adjust to technical or organizational constraints while keeping stakeholders aligned
• Define adoption and impact metrics before deployment and own measurable outcomes
• Translate business outcomes into technical implementation and technical constraints into business decisions
• Design POC scope, success criteria and graduation paths into production
• Own the case for expansion alongside the account team
• Identify additional optimization opportunities in client environments
• Turn field learnings into reusable playbooks
• Provide technically specific product feedback to influence the roadmap
• Operate collaboratively and independently while supporting practice growth

Senior Serverless Spark Migration Engineer

Senior Compute Engineer, Go, Kube

Senior Deployment Strategist – Cloud & AI