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Open nowFirst seen 2 hours ago

Data Program Manager, Data Strategy

Google3,566 open roles

Where
Sunnyvale, CA, USA; Chicago, IL, USA
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Your applicationOpen nowData Program Manager, Data StrategyGoogle · Sunnyvale, CA, USA; Chicago, IL, USA

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on Google's own form.

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The clock on this job

Early applications get read.

9.5% of postings close within 7 days. Measured by our own scanner across the market. Google postings stay open a median of 28 days.

Share of postings closed within
  1. 1.8%1 day
  2. 3.9%3 days
  3. 9.5%7 days
  4. 15.5%14 days
  5. 33.9%30 days
This job: first seen 2 hours ago

Google median: 28 days open

The posting

Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Sunnyvale, CA, USA; Chicago, IL, USA.

Minimum qualifications:

  • Bachelor's degree in a quantitative discipline or equivalent practical experience.
  • 7 years of experience in business intelligence, analytics, or quantitative analysis related fields.
  • Experience programming with SQL, and an additional programming language (e.g. with R, Python, JavaScript).

Preferred qualifications:

  • Master’s degree in a technical or business-analytical field.
  • Experience working within Finance domains (e.g., Accounting, FP&A, Treasury) with an understanding of financial compliance and regulatory requirements.
  • Experience in management or technology consulting, leading data strategy or data architecture re-design.
  • Proven track record of managing enterprise data governance or digital transformation projects within a large-scale corporate environment.
  • Familiarity with Google’s technology stack for data processing (e.g., BigQuery, Spanner, F1) and Cloud data architecture.
  • Excellent communication skills, with the ability to advocate for data-first thinking to both technical and non-technical executives.

About the job

At Google, data drives all of our decision-making. As part of the Finance Data and Analytics (DnA) team, you will drive programs that empower Finance with trusted and secure data that is governed and easily discoverable. This work is high-stakes: Finance data is among the most sensitive at Google, and is created and consumed in different shapes and configurations both across Finance and within every other Business Area.

As a Data Program Manager on the Finance Data and Analytics team, you will serve as the strategic bridge between Google Finance business teams and technical data capabilities. Your primary focus will be immersing yourself in the operational and strategic needs of the Finance organization to architect a modern, AI-ready Data Strategy.

Applying a Data Product Management lens, you will treat financial data domains as scalable products discovering user pain points, defining product roadmaps, and prioritizing capabilities that unlock value. You will lead high-impact programs that translate complex financial workflows into data initiatives and identify agentic use cases that drive true process transformation across Google Finance. By orchestrating execution across Finance and Engineering, you will ensure our data ecosystem is structurally sound and primed for the future of agentic AI.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $148000 - $214000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

  • Partner deeply with Finance units (FP&A, Treasury, Tax, Accounting) to map end-to-end workflows, diagnose friction, and translate challenges into clear data and product requirements.
  • Own the lifecycle, vision, and roadmaps for core financial data domains, treating datasets and semantic layers as high-value internal data products.
  • Define and execute data roadmaps tailored to feed LLMs, predictive models, and autonomous agents with clean, standardized, contextual financial data.
  • Partner with Finance SMEs and Data Engineering to define unified taxonomies, standardized KPIs, and robust data governance embedding SOX and GDPR compliance.
  • Oversee end-to-end modernization programs, driving trust, literacy, and tool adoption while defining metrics to measure efficiency gains, accuracy improvements, and business ROI.
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