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Google New York, NY

New York, NY

Today

$106k - $151k/yr Full time Backend Developer Analytics Artificial Intelligence

Build scalable data pipelines and reporting tools on Google’s GCS Data Science team in New York, NY (onsite).

Develop data pipelines, reporting assets, and reusable best practices/frameworks for analytics and stakeholder enablement across the organization.
Apply feature engineering to support ML/AI model development, including ETL pipeline development for downstream scaling.
Improve pipeline quality by adopting best practices for data integrity, test design, analysis, validation, and documentation.
Create scalable, actionable solutions including dashboards, automated collateral, and web applications that surface insights for advertisers.
Collaborate with stakeholders to identify feature and tooling gaps and drive customer-focused improvements.

Bachelor’s degree in Computer Science or a related technical field (or equivalent practical experience).
At least 1 year of experience designing data pipelines (ETL) and model data.
Experience analyzing data and producing reports.
Experience with database query (for example, SQL) and visualization tools (for example, Tableau and dashboards).
Experience with one or more general purpose programming languages (for example, Python, C/C++, Java).

Experience in a data science environment supporting feature engineering and model automation needs.
Experience with big data tools, distributed computing, and non-relational databases.
Structured thinking for breaking down ambiguous problems and proposing impactful data modeling designs.
Interest in analyzing large and complex datasets and translating findings into information that drives business decisions.

The GCS Data Science team works on challenging problems for Google Customer Solutions (GCS) within Global Business Organization (GBO).
Goal: build efficient and scalable ML models to help small and midsize businesses grow using Google solutions.
The Data Science Engineering (DSet) subteam supports data engineering needs for GCS Data Science teams, including new data pipelines, automation, observability, and reporting tools.
Use an agile approach to tackle big data challenges and apply an analytical, data-driven method to understand a fast-changing business.
Build data pipelines and reporting tools that enable data scientists through feature engineering and automated data extracts and wrangling, delivering scaled insights for both the science team and users.
GCS sales teams focus on customer success for small- and medium-sized businesses (SMBs) by bringing Google solutions to customers.

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