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Principal Specialist- Data Science Customer Programs
RTX
Bengaluru, IND
Full-time
Senior · 5+ years experience
Hybrid
Discovered Yesterday
DatabricksApache SparkSQLMicrosoft Power BITableauPython
Free
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DatabricksApache SparkSQL
Key Skills for This Role
DatabricksApache SparkSQLMicrosoft Power BITableauPython
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Who We Are
At RTX, the world 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
- The P&WC engine programs are powering 5 engine markets: Regional turboprop aircrafts, General aviation aircrafts, Business jets aircrafts, Helicopters and Auxiliary power systems on larger aircrafts, which help connecting people and economies around the world.
- The Data Engineer plays a key role in enabling scalable and reliable data infrastructure for the Customer Programs Hub (CPH) organization. This role focuses on designing, developing, and maintaining robust data pipelines, scalable data platforms, and curated data layers that support analytics, operational reporting, and data governance initiatives.
- The role works closely with Data Analysts, Data Stewards, Digital Technology (DT) teams, and business stakeholders to ensure enterprise data is efficiently ingested, transformed, governed, and made accessible for advanced analytics and decision-making.
Responsibilities include
- Design, develop, and maintain scalable data pipelines and ETL/ELT workflows to ingest, transform, and process large operational datasets from multiple enterprise systems using platforms such as Databricks and Spark.
- Develop and maintain data ingestion frameworks for integrating structured and semi-structured data sources while optimizing pipeline performance for efficient large-scale data processing.
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