Automation Engineer - Data Governance

RTX•US-NC-REMOTE, NC

1d•$107,500 - $204,500•Remote

About The Position

The Data Governance Automation Engineer will design, develop, and maintain reusable automation solutions supporting RTX’s enterprise data governance strategy. Working with internal customers and cross-functional teams, the engineer will build APIs, ETL pipelines, and workflow automation to streamline metadata management, data profiling, and data quality processes. The role advances data governance capabilities through scalable, reliable, and maintainable engineering solutions that align long-term data strategies with immediate business needs. The position is remote and is part of RTX Corporate, Enterprise Services. Employees in remote roles work primarily offsite from home; a manager may discuss some onsite presence if the employee lives within reasonable commuting distance of an RTX site. Candidates may be asked to attend selected interview steps in person. U.S. citizenship is required because only U.S. citizens are authorized to access information under the program or contract. No security clearance is required. The salary range is $107,500–$204,500 USD. Compensation and benefits depend on factors including the role, responsibilities, experience, location, education, and skills. Eligible benefits may include medical, dental, vision, life and disability insurance, 401(k) match, flexible spending accounts, flexible work schedules, an employee assistance program, educational assistance, parental leave, paid time off, and holidays. Eligible applicants may also receive annual short- or long-term incentive compensation, which is not guaranteed.

Requirements

Typically requires a bachelor’s degree and at least 8 years of relevant experience, or an advanced degree in a related field and at least 5 years of experience.
Experience with scripting or software development languages such as Python, Java, or PowerShell.
Experience developing and integrating REST APIs, working with JSON, OAuth, and API contracts.
Experience with ETL development, data integration, and workflow orchestration.
Experience with SQL and cloud or data platform technologies.
Experience with data profiling, validation, and data quality automation.
Experience with version control, CI/CD pipelines, and automation testing.
U.S. citizenship is required.

Nice To Haves

3–6 years of experience in automation, data engineering, integration engineering, or software engineering.
Experience integrating metadata management and data catalog platforms.
Familiarity with workflow automation and event-driven architecture.
Experience implementing monitoring, logging, exception handling, and reconciliation for automated processes.
Strong problem-solving skills and attention to detail, with the ability to connect business processes and data management requirements to technical solutions.
Ability to balance long-term technical strategy with tactical delivery in an Agile environment.
Strong collaboration and communication skills, including the ability to work with internal customers and cross-functional stakeholders.
Experience contributing to data governance initiatives, roadmaps, or enterprise data management programs.

Responsibilities

Collaborate with internal customers and cross-functional teams to support RTX’s data and data governance strategy and align initiatives with enterprise data goals.
Design, develop, and maintain APIs, API contracts, scripts, and reusable automation components for data governance processes.
Automate metadata ingestion, scanning, synchronization, and workflow activities across data governance platforms.
Develop and maintain ETL pipelines for metadata processing and governance-related data movement.
Automate data profiling, validation, data quality rule execution, and integration of data quality results.
Build scheduled and event-driven automation to support onboarding and management of newly created data assets.
Implement logging, monitoring, retry logic, exception handling, and reconciliation processes to ensure reliable automation.
Test, deploy, troubleshoot, and maintain automation solutions for performance, reliability, and long-term maintainability.
Apply software engineering best practices, including version control, CI/CD, and automated testing.
Contribute to data governance roadmaps and tool requirements, identifying opportunities to improve data management processes and deliver business value.