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Описание вакансии
TL;DR
Analytics Engineer (Finance): Building and maintaining data infrastructure for the Finance organization with an accent on SQL, Python, and Snowflake-based data modeling. Focus on architecting performant data models, automating reporting pipelines, and ensuring data quality to empower strategic decision-making.
Location: Must be based in or near Menlo Park, CA; requires in-person attendance at least 3 days per week.
Salary: $132,000 – $173,000
Company
Snowflake is a cloud-based data platform company powering the era of the agentic enterprise.
What you will do
- Develop and maintain data infrastructure using SQL, Python, dbt, and Airflow.
- Architect flexible, performant data models to support Finance reporting and analysis.
- Perform data QA and implement automated testing procedures for data models.
- Collaborate with IT and technical stakeholders to source data from business systems.
- Contribute to data governance, security models, and data lineage strategies.
- Create and maintain user-friendly documentation for key metrics and data models.
Requirements
- 3+ years of experience as an analytics, data, or BI engineer.
- Advanced SQL skills with experience in large-scale relational datasets.
- Experience using Python for data parsing, structuring, and transformation.
- Experience with MPP databases like Snowflake, Redshift, or BigQuery.
- Ability to work in an agile development environment.
- Strong communication skills and ability to work with diverse stakeholders.
Nice to have
- Experience with ERP and Financial Planning tools.
- Familiarity with CI/CD pipelines and cloud cost management (FOCUS Framework).
- Experience supporting product finance stakeholders or cost attribution models.
- Demonstrated experience with AWS, Azure, or Google Cloud.
Culture & Benefits
- Opportunity to work in a fast-paced, innovative environment.
- Competitive compensation package including equity and commission.
- Focus on experimental mindset and AI-native problem solving.
- Collaborative team culture emphasizing low-ego and high-impact work.
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