Principal Software Engineer – Data Engineering

Caterpillar Inc.

. Define functional designs and application systems architecture across digital platforms .

Posted 9/24/2026full-timeChennai • IndiaLeadWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in AWS technologies, data architecture, and application systems design, with a strong focus on developing and optimizing data solutions for manufacturing and supply operations. Proven ability to lead cross-functional teams, document solutions, and enhance digital capabilities through emerging technologies.

Highest-signal resume keywords

AWS ExpertiseData Architecture LeadershipSQL ProficiencyPython DevelopmentSnowflake Data Solutions

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Data ModellingImpact AnalysisTechnical TroubleshootingData Quality ImprovementHLA DocumentationTA DocumentationData Lake SolutionsData Pipeline DesignMicroservices KnowledgeAPI Knowledge

Soft Skills

Stakeholder CommunicationCross-Functional CollaborationTechnical Solution GuidanceRelationship BuildingMeeting Facilitation

Tools & Technologies

ConfluenceNVIDIA OmniverseUSD Data ModelsIoT IntegrationMachine Telemetry

Industry Keywords

Digital PlatformsData SolutionsManufacturing OperationsSupply ChainIndustrial IoT

Tech Stack

Tools & technologies

AWSDynamoDBIoTMicroservicesPythonSQL

About the role

Key responsibilities & impact

Define functional designs and application systems architecture across digital platforms
Lead data architectures for large digital platform applications
Develop detailed architecture deliverables to solve business problems
Design application technical infrastructure, including databases, programming languages, utilities, and testing approaches
Lead evaluation and deployment of new technologies to enhance digital technical capabilities
Address application business requirements and collaborate with cross-functional teams to deliver digital solutions
Run architecture meetings and document solutions in Confluence
Complete solutions and facilitate engineering handover
Support engineering teams throughout build and deployment phases
Develop and optimize data products and data-domain solutions for manufacturing and supply operations
Improve architecture by tracking and evaluating emerging technologies
Create and review HLA and TA documentation
Identify solutions to improve data quality in the platform

Requirements

What you’ll need

Expertise on AWS (EMR, Glue, S3, Fargate, SNS, SQS, Kinesis, AWS EventBridge, RDS, DynamoDB), Snowflake, SQL, Python, and ER Modelling
Strong understanding of the data platform and ability to build new data solutions on existing data platforms
Ability to perform impact analysis across data solutions
Understanding of current data landscapes and existing solutions
Demonstrable experience troubleshooting and resolving technical and functional issues
Ability to evaluate, analyze, document, and communicate business requirements to stakeholders
Experience running architecture meetings/discussions and documenting solutions on Confluence
Experience supporting engineering teams through build and deployment phases
Experience developing partner relationships with customers, dealers, Technical Product Management, and architecture teams
Experience providing technical solution guidance and database recommendations
Experience owning data lake solutions in Snowflake that are performant, secure, and cost optimal
Experience owning or reviewing data domains in a data platform
Experience providing ROM (rough order of magnitude) estimates for solutions and data products
Experience creating reusable data products from data-domain and business requirements
Experience creating/reviewing HLA and TA documentation
Experience evaluating emerging technologies against business and operational requirements
Experience identifying solutions for business-critical data-quality rules
Nice to have: Microservices and API knowledge
Nice to have: Working knowledge of NVIDIA Omniverse, USD data models, and connector frameworks
Nice to have: Experience designing end-to-end data pipelines integrating IoT, sensor, and machine telemetry data
Nice to have: Familiarity with digital twin architecture patterns
Nice to have: Understanding of Industrial IoT ecosystems