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Automotive Robotics India Private Limited ARi
Automotive Robotics India Private Limited ARi cover picture
Automotive Robotics India Private Limited ARi logo

Automotive Robotics India Private Limited ARi

https://arigs.com/services
Founded :
2001
Type :
Services
Size :
1000-5000
Stage :
Profitable

About

ARi is a prominent provider of integrated engineering solutions, industrial automation, and digital transformation services. Founded in 2001 and operating under the ARi brand since 2006, the company is headquartered in East Peoria, Illinois, and has a global presence with additional locations across North America and the Asia Pacific.

With a workforce of 5,001-10,000 employees, ARi generates approximately $274.7 million in annual revenue. The company offers a wide range of services, including product engineering, software development and testing, manufacturing engineering, testing and validation, industrial automation and robotics, and digital transformation strategies. ARi also focuses on smart manufacturing, helping businesses transition to more advanced production methods. Their portfolio includes high-quality industrial automation and robotics products, supported by specialized centers for electrification testing and automation. ARi is guided by core values of integrity, collaboration, excellence, innovation, and transparency. 

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Candid answers by the company

What is the location preference of jobs?

Bangalore, Chennai, Hyderabad, Pune

Company social profiles

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Jobs at Automotive Robotics India Private Limited ARi

Automotive Robotics India Private Limited ARi
at Automotive Robotics India Private Limited ARi
1 candid answer
Sreenivasulu Kommineni
Posted by Sreenivasulu Kommineni
Bengaluru (Bangalore), Chennai
4 - 7 yrs
₹6L - ₹8L / yr
data engineering, data platforms, or data centric
experience designing and building data pipelines,
Advanced proficiency in SQL, Python, and PySpark
Proven experience developing and supporting produc
Expertise in ELT/ETL design, performance optimizat
+3 more

Position Summary

The GCP Senior Data Engineer is responsible for designing, building, optimizing, and maintaining high‑quality data pipelines and cloud‑native data platforms on Google Cloud Platform (GCP). This role partners closely with data analysts, data scientists, business teams, DevOps, and platform engineering to deliver secure, reliable, and scalable data solutions that support Trane Technologies’ digital strategy.

This position requires strong hands‑on engineering capabilities, cloud experience, and a passion for delivering enterprise‑grade data solutions.Qualifications

Education Requirements

• Bachelor’s /Master’s degree in Computer Science, Information Technology, Data Engineering, Software Engineering, or a related technical field.

Experience Requirements

• 7+ years of total experience in data engineering, data platforms, or data‑centric software engineering.

• 5+ years of hands‑on experience designing and building data pipelines, ETL/ELT workflows, and cloud‑native data solutions.

• 3+ years of direct experience with Google Cloud Platform (GCP) using services such as BigQuery, Cloud Storage, Cloud Run, Datafusion, Dataproc, Composer, and Pub/Sub.

• Advanced proficiency in SQL, Python, and PySpark for data processing and transformation.

• Proven experience developing and supporting production‑grade, end‑to‑end cloud data pipelines.

• Expertise in ELT/ETL design, performance optimization, and data transformation frameworks.

• Strong background in monitoring, logging, alerting, and data quality frameworks across distributed systems.

• Experience with DevOps tools and techniques, including CI/CD, GitHub, and related automation practices.

• Demonstrated experience working in global, cross‑functional, and agile development environments.

• Excellent communication, problem‑solving, and analytical skills with the ability to collaborate across technical and non‑technical teams.

• GCP certification preferred (e.g., Google Cloud Professional Data Engineer).

Core Responsibilities

1. Data Engineering & Cloud Development (Core Responsibilities)

• Design, build, and maintain scalable data processing systems on Google Cloud Platform (GCP).

• Develop end‑to‑end data pipelines to support ingestion, transformation, and storage.

• Build ELT/ETL workflows, database objects, and cloud‑native data orchestration solutions.

• Optimize pipelines for performance, scalability, cost efficiency, and reliability.

• Ensure robust monitoring, alerting, and data quality assurance across distributed systems.

• Implement secure data architectures following Trane Technologies’ standards.

 

2. Collaboration & Analytics Enablement

• Work closely with data analysts and data scientists to understand business requirements and translate them into scalable data solutions.

• Support analytical use cases, data modelling, and enable self‑service data consumption.

• Partner across global teams, including business stakeholders, product owners, and data governance.

 

3. DevOps / Platform Engineering Partnership

• Partner with DevOps and platform engineering teams to ensure data infrastructure is secure, reliable, and highly available.

• Apply working knowledge of CI/CD practices (code versioning, automation, testing frameworks) to support cloud‑based deployments.

• Collaborate on environments, pipelines, and promote code through dev → test → prod.

4. Engineering Excellence & Ways of Working

• Participate in code reviews and maintain global coding standards.

• Follow structured engineering methods, documentation practices, and release processes.

• Contribute effectively in distributed, agile development teams across multiple time zones.

• Communicate clearly with both technical and non‑technical stakeholders.

 

5. Innovation & Continuous Improvement

• Research, evaluate, and propose new tools, technologies, and design patterns.

• Stay current with cloud, data engineering, and analytics trends relevant to enterprise‑scale environments.

• Apply continuous improvement practices to enhance reliability, quality, and developer productivity.

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