Senior Data Engineer
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Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 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.).
- 5 years of experience coding in one or more programming languages.
- 5 years of experience working with data infrastructure and data models by performing exploratory queries and scripts.
Preferred qualifications:
- 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.
- 3 years of experience developing project plans and delivering projects on time within budget and scope.
- 3 years of experience partnering with stakeholders (e.g., users, partners, customer), and managing stakeholders/customers.
- Experience with Machine Learning for production workflows.
About the job
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.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.
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 undertanding 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.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $156000 - $227000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Identify the underlying need, process datasets, and apply advanced data engineering, data modleing, and architectural frameworks when needed.
- 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 reliability monitoring.
- Operate with a high degree of autonomy, owning 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.
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