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Multi Engine Pilot Jobs in Chicago, IL (NOW HIRING)

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Multi Engine Pilot information

See Chicago, IL salary details

$51K

$135K

$207.2K

How much do multi engine pilot jobs pay per year?

As of Aug 8, 2026, the average yearly pay for multi engine pilot in Chicago, IL is $134,969.00, according to ZipRecruiter salary data. Most workers in this role earn between $103,100.00 and $159,800.00 per year, depending on experience, location, and employer.

What is a multi engine pilot?

Multi engine pilots are licensed aviators trained and certified to operate aircraft equipped with more than one engine. They possess specialized knowledge and skills to handle the complexities of multi engine aircraft, such as managing engine failures, asymmetric thrust, and advanced systems. Multi engine pilots typically undergo additional training and testing beyond the requirements for single engine pilots. They are often employed in commercial aviation, corporate flight departments, and charter services where multi engine planes are commonly used.

What does a multi engine pilot do?

A multi-engine pilot is a pilot with credentials that allow them to operate an aircraft with more than one engine. The Federal Aviation Administration (FAA) has a recommended syllabus for institutions offering this training. As a pilot with the multi-engine land (MEL) rating, you can fly larger planes, whether as a private or commercial operator. You must perform several duties before and after flights, including inspection of the airplane, flight plan submission, and maintenance of safe operation during flight. Federal regulations dictate the maximum amount of hours you can fly, but you typically work outside of the standard 9-to-5 weekday schedule.

What are the key skills and qualifications needed to thrive as a multi engine pilot, and why are they important?

To thrive as a Multi Engine Pilot, you need advanced piloting skills, a thorough understanding of aerodynamics and aircraft systems, and a Multi-Engine rating on your pilot's license. Proficiency with avionics, flight management systems, and navigation tools is essential, along with familiarity with FAA regulations. Exceptional situational awareness, decision-making, and communication skills help pilots manage complex situations and coordinate with crew and air traffic control. These skills and qualifications are vital to ensure flight safety, efficient aircraft operation, and successful mission outcomes in multi-engine environments.

What is the difference between Multi Engine Pilot vs Commercial Pilot?

AspectMulti Engine PilotCommercial Pilot
Required CertificationsMulti-Engine Rating, Commercial Pilot CertificateCommercial Pilot Certificate, often with Multi-Engine Rating
Work EnvironmentOperate multi-engine aircraft, often for charter, cargo, or corporate flightsPerform commercial flights, including passenger and cargo services
Employer & Industry UsageAirlines, charter companies, corporate flight departmentsAirlines, charter services, cargo carriers, flight schools

The Multi Engine Pilot certification is a specialized qualification that allows pilots to operate aircraft with more than one engine. While a Commercial Pilot license enables pilots to be paid for flying, a Multi Engine Pilot rating is often a requirement for commercial operations. Many pilots obtain both certifications to expand their career opportunities in the aviation industry.

What are some common challenges faced by multi engine pilots during flight operations?

Multi engine pilots often encounter challenges such as managing asymmetric thrust during engine failures, coordinating complex cockpit procedures, and staying proficient with emergency checklists. Operating larger, faster aircraft also requires strong situational awareness and communication skills, especially when working with co-pilots and air traffic control. Regular training and clear communication are essential for safely handling the increased workload and potential in-flight issues unique to multi engine operations.
More about Multi Engine Pilot jobs
What are the most commonly searched types of Multi Engine Pilot jobs in Chicago, IL? The most popular types of Multi Engine Pilot jobs in Chicago, IL are:
What are popular job titles related to Multi Engine Pilot jobs in Chicago, IL? For Multi Engine Pilot jobs in Chicago, IL, the most frequently searched job titles are:
What job categories do people searching Multi Engine Pilot jobs in Chicago, IL look for? The top searched job categories for Multi Engine Pilot jobs in Chicago, IL are:
Multi Engine Pilot jobs near you
Infographic showing various Multi Engine Pilot job openings in Chicago, IL as of August 2026, with employment types broken down into 83% Full Time, and 17% Part Time. Highlights an 83% In-person, and 17% Remote job distribution, with an average salary of $134,969 per year, or $64.9 per hour.

Dynatrace Consultant / Observability Architect

Qualtrix Consulting

Arlington Heights, IL • On-site

$169K/yr

Other

Re-posted yesterday

Job description

Job Title: Dynatrace Davis AI Observability Architect (AWS) 
Location: Chicago, IL (Hybrid/Onsite Preferred) 
Duration: Long-Term Contract 
Experience: 10+ Years (Minimum 5+ Years in Dynatrace Architecture, 2+ Years applied Davis AI ) 
Employment Type: Contract / Full-Time 
Job Summary 
We are seeking a Dynatrace Observability Architect whose core differentiator is deep, hands-on mastery of Davis AI 
(causal AI, anomaly detection, predictive analytics) and the emerging Dynatrace MCP Server ecosystem that 
connects Dynatrace''s live observability data to AI coding assistants and agentic workflows (Claude, GitHub Copilot, 
Cursor, Amazon Q, and similar MCP clients). This is an architect who can move Dynatrace beyond dashboards and 
into autonomous, AI-driven operations. 
This is not a support or administration role. We need someone who can design Davis AI-driven root cause 
automation and stand up MCP-based agentic workflows that give AI assistants safe, governed, real-time access to 
production telemetry. 
Davis AI — Core Responsibilities 
• Architect and tune Davis AI causal analysis across infrastructure, application, and business layers — not 
just consuming default anomaly alerts, but customizing baselines, thresholds, and event correlation rules. 
• Design and implement Davis AI-driven root cause automation, reducing MTTR by connecting Davis
detected problems directly to remediation workflows. 
• Configure Davis anomaly detection for custom metrics, business events, and multi-dimensional analysis 
(not just infrastructure defaults). 
• Build and maintain Grail-based data models that feed Davis AI with high-quality, contextualized signals 
(proper tagging, management zones, Smartscape topology) — data quality directly determines Davis AI 
accuracy. 
• Implement Davis CoPilot (GA) and evaluate Davis CoPilot APIs (preview) for natural-language DQL 
generation, query explanation, and AI-assisted troubleshooting. 
• Define and tune predictive/forecasting use cases using Davis AI (capacity forecasting, anomaly prediction 
ahead of customer impact). 
• Own Davis AI governance: false-positive tuning, alert noise reduction, and continuous model feedback 
loops. 
• Translate Davis AI findings into automated Workflow Automation Engine actions (auto-remediation, ticket 
creation, Slack/Teams/PagerDuty routing). 
MCP Servers & Agentic AI Integration — Core Responsibilities 
• Architect and deploy the Dynatrace MCP Server (local/stdio via @dynatrace-oss/dynatrace-mcp-server 
and/or the new Remote Dynatrace MCP Server) to connect Dynatrace''s observability platform to AI 
coding assistants and agents. 
Dynatrace Consultant Architect JD  
• Configure secure, governed access using Platform Tokens / OAuth clients with least-privilege scopes 
(Grail query permissions, security-problem read access, etc.) for MCP clients. 
• Integrate the Dynatrace MCP Server with Claude Code, Claude Desktop, Claude in Chrome/Cowork, 
GitHub Copilot (VS Code), Cursor, Amazon Q Developer CLI, and Windsurf to bring live production 
context (logs, traces, problems, security events) directly into developer and agentic workflows. 
• Enable natural-language-to-DQL workflows via MCP so engineers and AI agents can query Grail (logs, 
events, spans, metrics) conversationally. 
• Design agentic incident-response patterns: AI agents that fetch problem/vulnerability details, correlate 
with recent deployments, and propose remediation — with human-in-the-loop approval for any state
changing action. 
• Establish cost governance for MCP-driven Grail queries (query budgets, 
DT_GRAIL_QUERY_BUDGET_GB, scoped time windows) since natural-language and agentic queries 
can scan large data volumes. 
• Monitor and audit MCP tool usage via Grail Business Events (com.dynatrace-oss.mcp.*) — track which 
agents/clients connect, which tools they invoke, and error rates. 
• Evaluate and pilot dtctl (open-source Dynatrace CLI) alongside the MCP server for agent-driven 
dashboard, workflow, and DQL management. 
• Build internal enablement/playbooks for engineering teams adopting MCP-connected AI assistants, 
including security review of agent permissions. 
Supporting Dynatrace Platform Responsibilities 
• Design and implement enterprise-wide observability architecture using Dynatrace across AWS 
environments (EC2, ECS, EKS, Lambda, Fargate, RDS, DynamoDB, S3, CloudFront, VPC, CloudWatch). 
• Configure OneAgent, ActiveGate, Management Zones, tagging standards, Smartscape, and PurePath as the 
topology/data foundation Davis AI and MCP tooling depend on. 
• Configure Synthetic Monitoring, RUM, Session Replay, DEM, APM, Log Monitoring, and 
Security/Runtime Vulnerability Analytics. 
• Integrate with ServiceNow, Jira, Slack, Microsoft Teams, PagerDuty, Splunk, Grafana, Prometheus. 
• Configure OpenTelemetry, distributed tracing, and Extensions Framework 2.0. 
• Implement Configuration as Code (Monaco) and Dynatrace Operator for Kubernetes. 
• Define SLOs/SLIs, error budgets, and RBAC/security architecture. 
Automation & DevOps 
Terraform, CloudFormation, Ansible, Jenkins, GitHub Actions, GitLab CI/CD, Kubernetes, Docker, Helm, Python, 
Bash, Node.js (v24+ required for local MCP server), REST APIs. 
Required Technical Skills 
Dynatrace Consultant Architect JD  
Dynatrace, Davis AI, Davis CoPilot, MCP (Model Context Protocol) / Dynatrace MCP Server, Grail (DQL), AWS, 
Kubernetes, Docker, Linux, Python, Node.js, Java, REST APIs, Microservices. 
Nice to Have 
• Prior hands-on experience deploying an MCP server (Dynatrace or otherwise) in a production or enterprise 
dev environment. 
• Experience with Claude Code, GitHub Copilot, Cursor, or similar AI coding agents in an enterprise setting. 
• Dynatrace Professional/Associate Certification, AWS DevOps Engineer Professional, CKA/CKAD. 
Soft Skills 
• Ability to explain Davis AI causal reasoning and MCP-driven agentic workflows to both engineers and 
executive leadership. 
• Strong judgment on AI-agent governance — knowing when autonomous action is appropriate vs. requiring 
human approval. 
• Ability to lead architecture discussions and mentor teams on both classical observability and emerging 
agentic-AI integration patterns. 
Preferred Experience 
• 10+ years IT experience; 5+ years enterprise Dynatrace architecture; 5+ years AWS. 
• Direct, hands-on Davis AI tuning experience (not just consuming default anomaly detection). 
• Practical experience standing up or integrating an MCP server — ideally the Dynatrace MCP Server — 
with at least one AI coding/agent client.

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