Cloud Data Engineer I, Professional Services, Google Cloud

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.

The Merchant Data Science team is a group of data scientists and engineers (US, London, Zurich) within the Merchant Shopping organization. We work on building scalable data products that empower data-driven decision-making.

In this role, you will work at the cross-section of data and tools engineering using genAI to help Google build durable data products. You will be a full-stack expert who can bridge the gap between software engineering, data engineering, and data science.

Google Ads is at the forefront of AI innovation, applying cutting-edge machine learning and Generative AI models like Gemini to power a multi-billion dollar global business.

Our work directly impacts billions of users by protecting users from harm, improving ad quality, and optimizing campaigns for advertiser return-on-investment. We foster a culture of deep collaboration, partnering closely with teams like Google Research and DeepMind to solve complex challenges. Join us to work on state-of-the-art AI, take on problems at an unparalleled scale, and build the next generation of advertising technology.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $130000 - $187000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google (https://www.google.com/about/careers/applications/benefits/).

Minimum qualifications:

- Bachelor's degree in Computer Science, Mathematics, a related field, or equivalent practical experience.

- 3 years of experience with data processing software (e.g., Hadoop, Spark, Pig, Hive) and algorithms (e.g., MapReduce, Flume).

- Experience with database administration techniques or data engineering, as well as writing software in Java, C++, Python, Go, or JavaScript.

- Experience building and maintaining production data pipelines that rely on multiple upstream data sources.

- Experience writing automated tests and validation for data pipelines.

Preferred qualifications:

- Experience in technical consulting.

- 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 Server Platform, Compute infrastructure, gcl, Spanner, or Flume.

- Own challenging, non-routine problems end-to-end: identify the underlying need, process complex datasets, and apply advanced data engineering, data modleing, and architectural frameworks when needed.

- Proactively design, build, and scale innovative data products, including self-serve tools, and automated pipelines,.

- Advance data infrastructure, product quality, and foundational understanding through automated validation frameworks, data quality, and reliabilyt monitoring.

- Operate with a high degree of autonomy, owning complex data engineering projects from initial conception to landing and impact

- Advocate impactful data products while contributing to a team culture that values engineering excellence, robust data, and sharp communication.