dennisthiessen
cad2c230eb
Two applications sent and finalized on 2026-06-15: - Google - Senior Data Engineer (Merchant Data Science, Zurich), 85.5/100. Tier-1 scope fix + both Tier-2 polish applied: re-scoped the Swisscom migration claim in resume B2 + CL P2 (Scope-Discipline), added project- delivery vocab (B4), and JD-exact 'distributed data processing' (B5). - Kraken (Payward) - SRE, AI Agents (remote, CH-eligible), 87.2/100. Finalized as-is; crypto-native + production-ML edge, honest infra gaps. Logs both as 'applied' in job_scout/state/decisions.json and flips their CLAUDE.md Active Sessions rows to SENT. Open item for both: confirm level and comp clear the 180k+ all-in bar at the recruiter stage. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
47 lines
3.5 KiB
Plaintext
47 lines
3.5 KiB
Plaintext
Senior Data Engineer — Google (Merchant Data Science, Merchant Shopping Organization)
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JD source: live scrape 2026-06-15 via Playwright (Google careers board), re-verified live same day
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URL: https://www.google.com/about/careers/applications/jobs/results/87066954308690630-senior-data-engineer?location=Switzerland
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Location: Mountain View, CA, USA; Zürich, Switzerland (preferred-location choice at apply)
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Level chip: "Mid" (title says Senior — clarify L4 vs L5 at recruiter stage)
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Comp (US band shown): $156,000 - $227,000 USD + 15% bonus target + equity + benefits. Zürich band NOT posted — verify clears 180k+ all-in.
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--- VERBATIM POSTING TEXT ---
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Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mountain View, CA, USA; Zürich, Switzerland.
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Minimum qualifications:
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Bachelor's degree or equivalent practical experience.
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5 years of experience designing data pipelines, and dimensional data modeling for synch and asynch system integration and implementation using internal (e.g., Flume, etc.) and external stacks (DataFlow, Spark, etc.).
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5 years of experience coding in one or more programming languages.
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5 years of experience working with data infrastructure and data models by performing exploratory queries and scripts.
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Preferred qualifications:
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5 years of experience with statistical methodology and data consumption tools such as business intelligence tools, collabs, jupyter notebooks, Tableau, Power BI, DataStudio, and business intelligence platforms.
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3 years of experience developing project plans and delivering projects on time within budget and scope.
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3 years of experience partnering with stakeholders (e.g., users, partners, customer), and managing stakeholders/customers.
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Experience with Machine Learning for production workflows.
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About the job
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The Merchant Data Science team is a group within the Merchant Shopping Organization. We work on building scalable data products that empower data-driven decision-making.
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In this role, you will innovate and build durable, impactful data products. You will bridge the gap between software engineering, data engineering, and data science.
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As a Data Engineer in the Merchant Shopping organization, you will build data products and foundations to improve Google's Shopping products. You will collaborate with a multidisciplinary team of data scientists, engineers, and PMs on a wide range of problems. You will bring an understanding of data, logging, and engineering. You will solve non-routine problems, build reliable data products used across the organization, and drive impact on cross-functional projects.
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Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
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US: $156000 - $227000 (USD) + 15% bonus target + equity + benefits
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Responsibilities
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Identify the underlying need, process datasets, and apply advanced data engineering, data modeling, and architectural frameworks when needed.
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Design, build, and scale innovative data products, including self-serve tools, and automated pipelines.
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Advance data infrastructure, product quality, and foundational understanding through automated validation frameworks, data quality, and reliability monitoring.
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Operate with a high degree of autonomy, owning data engineering projects from initial conception to landing and impact.
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Advocate impactful data products while contributing to a team culture that values engineering excellence, robust data, and sharp communication.
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