Cloud Data and AI Engineer, Professional Services

Google Google · Big Tech · Reston, VA +1

This role involves guiding Public Sector customers in developing, configuring, and deploying data and AI solutions on Google Cloud. The engineer will provide architecture guidance, best practices, and support for customer implementations, including the development and deployment of ML models and integrations. The role also includes customer-facing activities like site visits, solution deployment, and educational workshops, as well as collaboration with Google's Product Management and Engineering teams.

What you'd actually do

  1. Be highly collaborative and work closely with data producers, consumers and Data and AI Engineering teams across public sector customers and teams to understand the data needs, provide consultation, and design and develop solutions.
  2. Analyze on-premise and cloud database environments, consulting on the optimal design for performance and deployment on Google Cloud Platform. Design, build, and maintain data and AI solutions.
  3. Create and deliver best practices recommendations, tutorials, blog articles, sample code, and technical presentations, adapting to different levels of key business and technical stakeholders.
  4. Translate business requirements into conceptual, logical, and physical data models.

Skills

Required

  • software development in Python, Java, or C++
  • relational database technologies
  • implementing data and AI solutions (including LLMs)
  • providing technical leadership to business stakeholders
  • education to partners
  • TS/SCI security clearance

Nice to have

  • database and AI integrations
  • MLOps
  • data warehousing
  • data pipeline development, including ETL and ELT
  • cloud databases such as RDS, Aurora, ElastiCache, CloudSQL, AlloyDB, Datastore, or Bigtable
  • database administration techniques including storage, clustering, availability, disaster recovery, security, logging, performance tuning, monitoring and auditing
  • developing, deploying, and managing machine learning models
  • database management tools for backups, recovery, snapshot management, sharding, partitioning
  • database performance tuning

What the JD emphasized

  • Active, or the ability to obtain, a TS/SCI security clearance.

Other signals

  • customer implementations of Google Cloud products
  • architecture guidance
  • best practices
  • data migration
  • capacity planning
  • implementation
  • troubleshooting
  • monitoring
  • consult with customers on how to best design their data and AI solutions
  • development and deployment of ML models
  • integrations with leading Google technologies
  • travel to customer sites to deploy solutions
  • deliver workshops to educate and empower customers
  • work closely with Product Management and Product Engineering to drive excellence in Google Cloud products and features