About the job

The AIM Data Science team provides quantitative support, market understanding and a perspective to our partners throughout the organization, in close collaboration with the Ads and Commerce Finance team. Our success is measured by our ability to help partners make better, faster decisions. We prioritize insights over purely academic analyses. We focus on the most critical business issues, particularly engaged positioning and product features to drive the most significant impact. We aim to scale our impact by empowering partners with the tools they need to answer their own questions through clean datasets, automated dashboards, and clear documentation. We are responsible for translating data into clear business logic. We invest in data infrastructure, documentation, and metric definitions to ensure consistency and reliability.

As a Data Engineering Manager for Analytics, Insights and Measurement (AIM) and Enterprise Platform, you will help drive the goal of how large advertisers buy Google and third-party advertising inventory efficiently. You will be responsible for setting the team's direction, managing stakeholder relationships.The US base salary range for this full-time position is $180,000-$267,000 + 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

• Align executive cross-functional Ads stakeholders on a cross-functional process and discipline for the quantitative attribution of impact on key product initiatives.

• Provide thought leadership to executive leadership through proactive contributions; consistently using insights and analytics to drive decisions and alignment throughout the product organization.

• Define and report key performance indicators and launch impact as part of regular business reviews and contribute to metric-backed annual quarterly objectives and key results (OKR) setting.

• Build an understanding of the data sets used by Enterprise Platforms and partner teams, collaborating with Engineering teams to identify and address instrumentation gaps, ensuring accurate data collection for key functionalities, with a focus on our most impactful features.

• Anticipate and address issues as a trusted authority and critical domain expert. Conducting end-to-end problem-solving, including analysis and business cases.

Minimum qualifications

• Bachelor's degree or equivalent practical experience.

• 10 years of experience working with data infrastructure and data models by performing exploratory queries and scripts.

• 5 years of experience coding in one or more programming languages, and designing data pipelines, and dimensional data modeling for synch and asynch system integration and implementation using internal and external stacks.

• 3 years of experience in a leadership role (e.g., technical leadership or people management, supervision, or team leadership).

Preferred qualifications

• Advanced degree in a quantitative field such as: Statistics, Computer Science, Engineering, Mathematics, Economics, or Physics.

• 10 years of experience with statistical data analysis (data mining and data querying, ), modeling, experimentation, and managing analytical projects.

• 7 years of experience with statistical data analysis, modeling, experimentation, and causal inference to solve product and business problems.

• 5 years of experience in developing and managing metrics or evaluating programs/products.

• Experience in running experimentation-based decision-making processes, both quantitatively (inference, stats, etc.) and organizationally (discipline, alignment, stakeholder management).

• Excellent programming skills in SQL, Python or R.