Data Engineer (Puerto Rico)

RTX•Aguadilla, PR

1d•Hybrid

About The Position

Collins Aerospace is looking for a Data Engineer to join their Puerto Rico team. Collins is a leader in technologically advanced, intelligent solutions that help redefine the aerospace and defense industry. With a comprehensive portfolio and deep technical expertise, we help customers meet the demands of the global market. Join us and help shape the future of aerospace and defense. This is a hybrid position based in Puerto Rico. Join Collins Aerospace to help us revolutionize the aerospace industry today! RTX is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or veteran status, or any other applicable state or federal protected class. RTX provides affirmative action in employment for qualified Individuals with a Disability and Protected Veterans in compliance with Section 503 of the Rehabilitation Act and the Vietnam Era Veterans’ Readjustment Assistance Act. Privacy Policy and Terms: Click on this link to read the Policy and Terms RTX is an aerospace and defense company that provides advanced systems and services for commercial, military and government customers worldwide. It comprises three industry-leading businesses – Collins Aerospace, Pratt & Whitney, and Raytheon. Its 195,000 employees enable the company to operate at the edge of known science as they imagine and deliver solutions that push the boundaries in quantum physics, electric propulsion, directed energy, hypersonics, avionics and cybersecurity. The company, formed in 2020 through the combination of Raytheon Company and the United Technologies Corporation aerospace businesses, is headquartered in Arlington, Virginia.

Requirements

Typically requires a University Degree and minimum 5 years prior relevant experience or an Advanced Degree in a related field and minimum 3 years of experience.
Must be a U.S. Citizen.
Experience in engineering pipelines that handle both structured and unstructured data sources, including enterprise content repositories and file systems.
Demonstrated ability to segment, classify, or secure data sets based on business, legal, or compliance requirements.
Proficiency in Python, SQL, and data processing frameworks (Spark, PySpark, Databricks, EMR, Glue, or similar).
Experience with cloud platforms (AWS, Azure, or GCP) and their native data services.
Strong understanding of ETL/ELT, distributed systems, data modeling, and secure data architecture principles.
Ability to work effectively in Agile teams and collaborate across technical and business functions.

Nice To Haves

Experience designing data environments where strict security boundaries, data separation, or access controls are essential.
Familiarity with document processing, OCR, text extraction, image handling, or large unstructured content repositories.
Background with data governance, lineage, auditability, and automated policy enforcement.
Experience integrating data from SharePoint, network file shares, and legacy content systems.
Knowledge of AI/ML data preparation, feature pipelines, or working with high-sensitivity data sets.
Experience with DevOps and IaC tools such as Terraform, CloudFormation, GitHub Actions, or Jenkins.
Prior experience in aerospace, defense, manufacturing, or other regulated industries.

Responsibilities

Design, build, and maintain data pipelines that ingest, transform, and deliver structured data (databases, ERP/CRM systems, operational systems) and unstructured data such as file shares, SharePoint repositories, images, documents, logs, and engineering artifacts.
Engineer data solutions that can logically and physically segment data domains based on business rules, regulatory requirements, and security boundaries.
Implement strict data access controls, classification rules, and data protection mechanisms to support secure data separation across environments.
Build scalable architectures that support analytics, AI/ML, digital products, and data-driven decision making within sensitive or highly partitioned data landscapes.
Partner with cross-functional stakeholders to translate complex business and compliance requirements into reliable, secure data engineering solutions.
Optimize data workflows for performance, observability, and resiliency across cloud and hybrid platforms.
Develop reusable patterns, automation, and governance-aligned data frameworks that accelerate secure data enablement across the organization.
Support production data systems with strong attention to data integrity, lineage, and access auditability.