Data Engineer - Secure Data Pipelines for AI/ML Hybrid

Collins Aerospace

Windsor Locks (CT)

Hybrid

USD 87,000 - 165,000

Full time

2 days ago

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Benefits offered by this job

Medical, dental, and vision insurance

Vacation and PTO

401(k) with matching contributions

Tuition reimbursement

Student loan repayment

Life insurance and disability coverage

Parental leave

Wellness programs

Job summary

Collins Aerospace is seeking a data engineer to design and maintain data pipelines that ingest, transform, and deliver both structured and unstructured data across enterprise systems. You will build scalable architectures, enforce data access controls, and support analytics and AI initiatives in a hybrid cloud environment.

The role requires strong Python and SQL skills, plus experience with Spark ecosystem and cloud services.

Qualifications

Typically requires a University Degree and minimum 5 years prior relevant experience or an Advanced Degree with at least 3 years in related field.
U.S. Citizenship required for access to program/contract.
Experience with pipelines handling structured and unstructured data from enterprise content repositories and file systems.
Ability to segment, classify, or secure data sets per business, legal, or compliance requirements.
Proficiency in Python, SQL, and data processing frameworks; cloud platform experience; ETL/ELT and secure data architecture.

Responsibilities

Design, build, and maintain data pipelines ingesting, transforming, and delivering data from databases, ERP/CRM, and file shares.
Engineer data solutions to segment data domains based on business rules and security boundaries.
Implement strict data access controls, classification rules, and data protection across environments.
Develop scalable architectures to support analytics, AI/ML, and data-driven decisions in partitioned landscapes.
Collaborate with cross-functional stakeholders to translate requirements into secure data engineering solutions.
Optimize data workflows for performance, observability, and resiliency across cloud and hybrid platforms.
Create reusable patterns and governance-aligned data frameworks to enable secure data enablement.
Support production data systems with data integrity, lineage, and access auditability.