Engineering Data Officer (Hybrid)

Date Posted:

2026-08-12

Country:

United States of America

Location:

US-CT-EAST HARTFORD-ETC ~ 400 Main St ~ BLDG ETC

Position Role Type:

Hybrid

U.S. Citizen, U.S. Person, or Immigration Status Requirements:

The ability to obtain and maintain a U.S. government issued security clearance is required.​ U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance

Security Clearance Type:

DoD Clearance: Secret

Security Clearance Status:

Active and existing security clearance required after day 1

At RTX, the world's largest aerospace and defense company, 185,000 great minds are united by purpose and inspired to make a difference solving the world’s most complex problems. With our three market leading businesses, world-class operations and investments in research and development, we offer capabilities and opportunity no one else can. Together, we push the boundaries of known science and find new ways to connect and protect our world. 

Pratt & Whitney is a world leader in the design, manufacture and service of aircraft engines and auxiliary power systems and has been revolutionizing modern flight for over 100 years. Join us and help shape the future of aerospace and defense.

What You Will Do:

Pratt & Whitney, a world leader in the design, manufacture, and service of aircraft engines Engineering Organization, is seeking an experienced and visionary M6 Engineering Data Officer to lead the transformation of data strategy and governance within our Engineering organization. This is a unique opportunity to drive innovation, implement cutting-edge data frameworks, and shape the future of data operations within Engineering.

• Lead the implementation of the Engineering Data Strategy to enable data-driven decision-making across the organization.
• Develop and deploy ontological frameworks to standardize and optimize data structures and relationships.
• Oversee the transformation of the data product creation processes to enhance efficiency, scalability, and alignment with business objectives.
• Establish and lead the Engineering Data Governance Network , ensuring compliance with regulatory requirements and alignment with enterprise-wide data governance standards.
• Collaborate with cross-functional teams, including Military Engines, Commercial Engines, PWC, the other Functional Domains and Bus, to ensure consistency and alignment with existing data governance roles and practices.
• Act as a thought leader and advocate for data-driven innovation within the Engineering organization, driving cultural change and fostering a data-centric mindset.
• Provide strategic guidance on emerging data technologies and trends to ensure Pratt & Whitney remains at the forefront of the aerospace industry.

Qualifications You Must Have:

• Bachelor’s degree in Engineering, Data Science, Computer Science, or a related field.
• Minimum of 12+ years of experience in engineering, data management, or a related field or 10+ years of experience with an Advanced Degree
• Demonstrated expertise in data strategy development , ontological frameworks , and data governance implementation.
• Travel up to 15%

Qualifications We Prefer:

• Master’s or Ph.D. in a related field.
• Experience in the aerospace or defense industry.
• Familiarity with advanced data analytics, machine learning, and artificial intelligence technologies.
• Data Engineering Platforms and Tools Cloud Platforms: Proficiency with cloud-based platforms such as AWS (Amazon Web Services) , Microsoft Azure , or Google Cloud Platform (GCP) , including services like S3, Redshift, BigQuery, Azure Data Lake, and Data Factory.
• Data Pipelines and ETL Tools: Experience with tools like Apache Airflow , Apache NiFi , Talend , or Informatica for building and managing data pipelines.
• Big Data Frameworks: Expertise in Hadoop , Apache Spark , Kafka , or similar frameworks for processing and streaming large-scale datasets.
• Database Management: Advanced knowledge of relational databases (e.g., PostgreSQL , MySQL , SQL Server ) and NoSQL databases (e.g., MongoDB , Cassandra , DynamoDB ).
• Data Modeling: Experience in designing and implementing data warehouses , data lakes , and data meshes using tools like Snowflake , Databricks.
• Containerization and Orchestration: Familiarity with Docker ,