Forward Deployed Engineer

Job not on LinkedIn

🕒 September 23

🇺🇸 United States – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

👷🏻‍♀️ Engineer

👻 Ghost score 15%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Nexus Cognitive

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2018

💼 Consulting

📣 Marketing

📦 Logistics

💰 Series unknown on 2024-07

Consulting • Marketing • Logistics

Nexus Cognitive is a composable intelligence layer and open data platform that sits above enterprise data systems to make data AI-ready without replacing existing infrastructure. It provides a universal query gateway, automatic enrichment, centralized governance, and a managed full‑stack service that enables rapid (30‑day) AI deployments, lower TCO, and enterprise-grade security and compliance. Nexus Cognitive powers revenue, customer, operational, and risk intelligence by unifying data across warehouses, lakehouses, and cloud platforms while preserving business context and minimizing tool sprawl.

📋 Description

• Embed with strategic customers from post-sale discovery through production rollout, stabilization, and knowledge transfer • Translate business outcomes and ambiguous customer requirements into technical designs, milestones, and working software • Design, build, test, and deploy customer-specific integrations, data pipelines, platform extensions, migration utilities, and AI-enabled workflows • Integrate NexusOne with enterprise identity, security, governance, data catalog, storage, compute, orchestration, observability, and CI/CD systems • Support migrations and modernization across on-premises, private-cloud, public-cloud, and hybrid environments • Establish production-readiness criteria for performance, reliability, security, disaster recovery, monitoring, and operational ownership • Write production-quality code and deploy and troubleshoot distributed systems on Kubernetes using infrastructure-as-code • Diagnose issues across application code, data pipelines, networking, IAM/RBAC, certificates, storage, compute, and customer infrastructure • Lead technical workstreams during high-severity deployment issues and drive root-cause resolution • Produce architecture diagrams, design documents, implementation plans, runbooks, test plans, decision logs, and post-incident reviews • Lead technical workshops, design reviews, whiteboarding sessions, and hands-on enablement • Pair with customer engineers and architects to enable independent operation of solutions • Provide structured field feedback to Product and Engineering • Convert repeated work into reusable connectors, reference architectures, deployment automation, tests, templates, and playbooks • Contribute product-level improvements to the core platform • Create clean handoffs to Customer Reliability Engineering and Support

🎯 Requirements

• 6+ years of professional software, platform, data, or infrastructure engineering experience, including substantial hands-on ownership of production systems • Experience in a customer-facing post-sales, professional services, solutions delivery, consulting engineering, resident engineering, or forward-deployed role—or equivalent experience working directly with enterprise users • Production proficiency in at least one backend language such as Python, Java, Scala, or Go • Experience with modern data infrastructure and distributed systems, such as Spark, Trino/Presto, Iceberg, Kafka, Airflow, dbt, data lakes, or cloud data platforms • Strong Kubernetes and container experience, including deployment, troubleshooting, configuration, networking, observability, and operational readiness • Working knowledge of at least one major cloud platform (AWS, Azure, or GCP) and infrastructure-as-code practices such as Terraform • Ability to troubleshoot across system boundaries and resolve underlying problems • Experience with enterprise constraints including security reviews, IAM/RBAC, private networking, compliance controls, change management, and multiple stakeholder groups • Excellent written and verbal communication • High agency, sound judgment, low ego, and comfort operating in ambiguous environments • Willingness to travel to customer sites as engagements require • Experience deploying data platforms in regulated industries, on-premises or hybrid-cloud environments, legacy data estates, production AI applications, performance engineering, high availability, disaster recovery, security hardening, or incident response are strong differentiators

🏖️ Benefits

• Collaborative team culture built on curiosity and respect • Challenging work where contributions clearly matter • Leadership team that invests in learning and development • Opportunity to work at the intersection of cloud, data, and AI innovation