Technical Solutions Engineer, Data Analytics

1 Month ago • 4 Years + • DevOps • Full Stack Development

Job Summary

Job Description

This Technical Solutions Engineer role focuses on Google Cloud's Data Analytics platform. Responsibilities include troubleshooting and resolving customer issues related to BigQuery, AI/ML, and underlying architectures; acting as a technical consultant for internal and external stakeholders; providing 24/7 global support; and collaborating with engineering and product teams to improve Google Cloud. The ideal candidate possesses strong coding skills (Java, C, C++, .NET, Python, Shell, Perl, JavaScript), extensive experience in technical support or related fields, and a deep understanding of data analytics and cloud technologies. The role involves working directly with customers on production deployments to ensure product readiness, availability, and scalability.
Must have:
  • Bachelor's degree in STEM or equivalent experience
  • 4+ years coding experience (Java, C++, Python, etc.)
  • 4+ years in technical support, software development, or product operations
  • Troubleshooting and root cause analysis
  • Technical consulting and problem-solving
  • Collaboration with internal and external teams
Good to have:
  • SQL database administration
  • Open source software experience
  • Cloud networking and distributed computing
  • Data analytics, warehousing, ETL development
  • Experience with Docker and Kubernetes
  • High SLA/SLO environment experience

Job Details


Minimum qualifications:

  • Bachelor’s degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
  • 4 years of experience reading or debugging code in one or more of the following: Java, C, C++, .NET, Python, Shell, Perl, JavaScript.
  • 4 years of experience in technical support, professional services, software development, or product operations management.

Preferred qualifications:

  • Experience with SQL database administration, open source software communities, cloud networking solutions, or distributed computing.
  • Experience in data analytics, warehousing, Extract Transform and Load (ETL) development, data science, or other Big Data applications.
  • Experience in technical consulting on database design and architecture supporting large enterprise customers with high Service Level Objective (SLO) and Service Level Agreement (SLA) requirements.
  • Experience working in distributed applications or micro-services in a cloud based environment (e.g., Docker, Kubernetes).
  • Experience working in a dynamic organization with extensive cross-collaboration across functions.

About the job

The Google Cloud team helps companies, schools, and government seamlessly make the switch to Google products and supports them along the way. You listen to the customer and swiftly problem-solve technical issues to show how our products can make businesses more productive, collaborative, and innovative. You work closely with a cross-functional team of web developers and systems administrators, not to mention a variety of both regional and international customers. Your relationships with customers are crucial in helping Google grow its cloud business and helping companies around the world innovate.

Responsibilities

  • Develop an understanding of Google Cloud's Data Analytics (e.g. BigQuery), AI/ML, and underlying architectures by troubleshooting, reproducing, and determining the root cause for customer reported issues, building tools, and faster diagnosis.
  • Act as a consultant and subject matter expert for internal stakeholders in Engineering, Sales, and customer organizations to resolve technical deployment obstacles and improve Google Cloud.
  • Work as a part of a team of Engineers/Consultants that globally ensure 24 hour customer support.
  • Understand customer issues, advocate for their needs with internal teams, including Product and Engineering teams, and drive high-quality production.
  • Work with customers on their production deployment to resolve issues and achieve product readiness, availability, and scale.

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