Durability Data Analytics Lead Engineer

Prattwhitney; East Hartford (CT); On-site; USD 107,500 - 204,500; Full time

Job summary

Pratt & Whitney in East Hartford, CT is seeking a motivated data engineer to join the Hot Section Durability group. You will design and optimize ETL pipelines in Databricks to unify data from multiple sources for fleet‑level analytics.

The role requires strong Python and SQL skills, data engineering know‑how, and the ability to mentor teammates. You will apply reliability analyses and AI/ML tools to improve life predictions and inform engineering decisions.

Qualifications

• Bachelor’s degree in STEM with 8+ years of engineering experience, or an advanced degree with 5+ years of related experience.
• Master’s Degree in Data Science or related field preferred.
• Proficiency in data engineering and database architecture.
• Knowledge of statistical analysis techniques, especially reliability analysis.
• Expert Python proficiency and collaboration in revision‑managed environments.
• Strong SQL skills and experience handling large datasets.
• Experience mentoring engineers and presenting to stakeholders.

Responsibilities

• Optimize and execute ETL pipelines in Databricks to integrate data into the data warehouse.
• Interrogate data to drive insights and apply AI/ML tools when appropriate.
• Use data visualization tools to present findings to technical and non‑technical stakeholders.
• Collaborate with cross‑functional teams to define data requirements and maintain data integrity.
• Advance durability life predictions and ensure consistency across analytics workflows.

Skills

Python

SQL

Data engineering

Statistical analysis

Data visualization

Mentoring

Education

Bachelor’s degree in STEM

Master’s degree in Data Science

Tools

Databricks

Data warehouse

Job description

Pratt & Whitney in East Hartford, CT is seeking a motivated data engineer to join the Hot Section Durability group. You will design and optimize ETL pipelines in Databricks to unify data from multiple sources for fleet‑level analytics.

The role requires strong Python and SQL skills, data engineering know‑how, and the ability to mentor teammates. You will apply reliability analyses and AI/ML tools to improve life predictions and inform engineering decisions.