Data Warehouse Engineer

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Requirements:

• Strong proficiency in SQL and Python.
• Deep understanding of data modeling concepts.
• Committed to data quality.
• Familiar with modern data platforms and best practices for scalability and performance.

Responsibilities:

• Develop and maintain efficient data pipelines using Airflow, Databricks, and dbt to transform raw data into analytics-ready datasets for user-facing products and insights.
• Implement automated testing and monitoring systems to uphold data quality and integrity, leveraging best practices in observability and issue detection.
• Partner with stakeholders across Zapier to understand their analytical needs and build data models and self-service analytics solutions using tools like Looker and Amplitude.
• Collaborate with product, marketing, and engineering teams to ensure products and services are properly instrumented to collect the right data for analytics.
• Contribute to architectural decisions, tooling choices, and strategies to optimize data storage, workflows, and schemas, ensuring scalability, accessibility, and security.
• Work closely with Data Scientists and cross-functional stakeholders to bridge the gap between raw data and actionable insights, empowering teams to make informed, data-driven decisions.
• Act as a resource for junior team members, offering guidance and knowledge-sharing to elevate team expertise and contribute to a culture of learning.