Systems Architect II

Align solution designs with data streaming platform capabilities and constraints across: Kafka (event backbone) Palantir Foundry (data modeling and operational analytics) Microsoft Fabric Real-Time Intelligence (real-time reporting and analytics)

Kafka (event backbone)

Palantir Foundry (data modeling and operational analytics)

Microsoft Fabric Real-Time Intelligence (real-time reporting and analytics)

Guide integration between: Data producers and consumers Streaming platforms and downstream systems Operational and analytical platforms

Data producers and consumers

Streaming platforms and downstream systems

Operational and analytical platforms

Guide consistent adoption of approved platform patterns across engineering teams

Define standard patterns for streaming integration, including: Topic design and partitioning strategies Schema management and data contracts Event design for cross-platform interoperability

Topic design and partitioning strategies

Schema management and data contracts

Event design for cross-platform interoperability

Establish reusable reference architectures and implementation patterns for engineering teams

Promote platform-first design over one-off custom solutions

Infrastructure as Code & Platform Automation

Define and standardize Infrastructure as Code patterns for provisioning and managing: Streaming infrastructure (Kafka clusters, topics, and supporting services) Platform integrations with Palantir Foundry and Microsoft Fabric

Streaming infrastructure (Kafka clusters, topics, and supporting services)

Platform integrations with Palantir Foundry and Microsoft Fabric

Guide the use of Terraform and ArgoCD (or equivalent tooling) for consistent, declarative, and repeatable deployments

Ensure infrastructure is: Version-controlled (source of truth in Git) Environment-consistent (dev/test/prod) Auditable and reproducible

Version-controlled (source of truth in Git)

Environment-consistent (dev/test/prod)

Auditable and reproducible

Promote automated environment provisioning over manual configuration

Define CI/CD and deployment patterns for streaming and platform solutions, including: Release orchestration, validation, and rollback Code deployment (applications, stream processing logic) Infrastructure deployment (IaC pipelines managed via Git workflows) Configuration and schema promotion across environments

Release orchestration, validation, and rollback

Code deployment (applications, stream processing logic)

Infrastructure deployment (IaC pipelines managed via Git workflows)

Configuration and schema promotion across environments

Establish standards for: Build, test, and release automation Environment promotion strategies Release governance, validation, and rollback strategies

Build, test, and release automation

Environment promotion strategies

Release governance, validation, and rollback strategies

Ensure integration of CI/CD pipelines with platform workflows across: Kafka ecosystems Foundry data pipelines Fabric real-time analytics components

Kafka ecosystems

Foundry data pipelines

Fabric real-time analytics components

Drive adoption of automated testing, validation, and continuous delivery practices

Define and promote AI-enabled patterns across data streaming platforms that create measurable value in architecture and operations, including: Real-time data enrichment and intelligent processing of event streams Integration of streaming data with AI/ML and agent-based workflows Event-driven triggers for AI-driven decisioning and automation

Real-time data enrichment and intelligent processing of event streams

Integration of streaming data with AI/ML and agent-based workflows

Event-driven triggers for AI-driven decisioning and automation

Guide integration of streaming platforms with AI capabilities within Foundry and Fabric, establishing scalable, reusable, and cost-conscious architecture patterns

Identify opportunities to leverage AI to improve data quality, anomaly detection, operational automation, and real-time decision support

Enable engineering teams to apply AI effectively in daily work through practical guidance in two areas: AI in the architecture — designing systems that are AI-ready, extensible, and aligned to performance, cost, and operational constraints AI in the team's practices — applying AI-assisted development, analysis, and operational tooling in practical, responsible ways

AI in the architecture — designing systems that are AI-ready, extensible, and aligned to performance, cost, and operational constraints

AI in the team's practices — applying AI-assisted development, analysis, and operational tooling in practical, responsible ways

Own architectural trade-offs including: Performance vs cost Build vs buy Platform utilization efficiency

Performance vs cost

Build vs buy

Platform utilization efficiency

Evaluate run cost, compute usage, and platform economics, including impacts from streaming volume, processing patterns, and AI usage

Ensure solutions are operationally sustainable and cost-effective

Provide architectural guidance to engineers, platform teams, and product partners

Lead design and architecture reviews for streaming and integration solutions

Mentor engineers in event-driven architecture, platform usage (Kafka, Foundry, Fabric), Infrastructure as Code, CI/CD, and practical use of AI tools in engineering and system design

Influence direction through technical expertise and clarity of design

Minimum Qualifications

Bachelor’s degree from an accredited institution required in Computer Science, Information Systems/Technology or related major field of study.

7 or more years of experience required in engineering/administration.

Equivalent Minimum Qualifications

High School diploma / GED.

9 or more years of experience in Information Systems.

Preferred Qualifications

Master's degree from an accredited institution required in Computer Science, Information Technology, or related major field of study.

5 or more years of experience in systems engineering/administration.

10 or more years of experience in Transportation/Intermodal Operations, Information Systems, or Logistics

Knowledge and Skills

Advanced knowledge of event-driven architecture, data streaming patterns, message broker design, and real-time integration concepts.

Strong understanding of Kafka-based architectures, including topic design, partitioning strategies, schema management, producers, consumers, and operational considerations.

Experience designing integrations across enterprise platforms, including Palantir Foundry, Microsoft Fabric Real-Time Intelligence, cloud services, application systems, and analytical environments.

Knowledge of Infrastructure as Code, GitOps, CI/CD, and automated deployment practices using tools such as Terraform, ArgoCD, Azure DevOps, GitHub, or equivalent technologies.

Ability to design scalable, secure, resilient, and cost-effective streaming solutions aligned to enterprise architecture standards and operational requirements.

Working knowledge of AI-enabled development, intelligent automation, real-time enrichment, anomaly detection, and AI/ML integration patterns within streaming and data platforms.

Strong analytical and architectural decision-making skills, including the ability to evaluate trade-offs across performance, cost, complexity, reliability, and time to delivery.