Google

Minimum qualifications:

• Bachelor's degree in Computer Science, Mathematics, a related field, or equivalent practical experience.
• 7 years of experience writing software in SQL, Java, C++, Python, Go, or JavaScript.
• Experience managing client-facing projects, troubleshooting technical issues, and working with Engineering and Sales Services teams.
• Experience in architecting cloud-native data solutions across cloud platforms, including design and implementation.

Preferred qualifications:

• Experience working with data warehouses, including data warehouse technical architectures, infrastructure components, ETL/ELT, and reporting/analytic tools and environments.
• Experience working with Big Data, information retrieval, data mining, or machine learning.
• Experience architecting, developing software, or internet scale production-grade Big Data solutions in virtualized environments.
• Experience with a broad range of data storage technologies, including advanced relational databases, MPP databases (e.g., BigQuery), NoSQL databases, and object storage.

About the Job

As a Technical Solutions Consultant, you will be responsible for the technical relationship of our largest advertising clients and/or product partners. You will lead cross-functional teams in Engineering, Sales and Product Management to leverage emerging technologies for our external clients/partners. From concept design and testing to data analysis and support, you will oversee the technical execution and business operations of Google's online advertising platforms and/or product partnerships.

You will be able to balance business and partner needs with technical constraints, develop innovative, cutting edge solutions and act as a partner and consultant to those you are working with. You will also be able to build tools and automate products, oversee the technical execution and business operations of Google's partnerships, as well as develop product strategy and prioritize projects and resources.

As a Data Architect, you will guide customers on how to ingest, store, process, analyze, explore, and visualize data on Google Cloud Platform. You will lead data migrations and transformations, partner with clients to architect scalable data processing systems, build efficient data pipelines, and resolve platform tests. In this role, you will collaborate with Google's cloud customers and our team to successfully implement Google Cloud products.

Applied AI builds conversational agents deployed at a large scale that achieve very meaningful results in the real world. Some examples include the customer agent built for large call center environments, to fast food ordering handled by our Food AI agent. The team is transforming how enterprises connect with customers through the power of AI. We also offer unique experiences for team members where you get to work directly with the model builders (Google DeepMind / Vertex), learn and work with brilliant AI leaders, and have access to Global 1000 customers via our existing Google Cloud relationships. The opportunity in this space is tremendous.

The US base salary range for this full-time position is $177,000-$263,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Demonstrated track record of successfully designing, implementing, and optimizing large-scale, and high-performance data ecosystems from conceptualization to production.
• Manage extensive experience with a broad range of data storage technologies, including advanced relational databases, MPP databases (e.g., BigQuery), NoSQL databases, and object storage.
• Architect cloud-native data solutions with expert-level proficiency in at least one major cloud platform including experience designing and implementing solutions.
• Master data integration patterns and leverage extensive experience with enterprise-grade ETL/ELT and orchestration tools.
• Implement data governance frameworks, data quality management, master data management, data lineage, metadata management, and robust data security/privacy principles with expert understanding.