
Red Bull North America is building the #1 CPG Data and Analytics team in the United States. We are looking for a Data Engineer to join our Enterprise Analytics & Data Engineering team - a small, high-ownership group serving Sales, Distribution, Operations, and Finance functions across the business.
The Data Engineer will play a pivotal role in transforming raw data into reliable, analytics-ready products that people actually use to make decisions, building and maintaining the pipelines, Snowflake data models, and dbt-based transformation layers that serve as the backbone of our analytics and AI ecosystem.
The ideal candidate will have hands-on experience with Snowflake, dbt, Dagster, and Python to develop, implement, and maintain robust data pipelines and analytical solutions. The engineer will interact directly with business stakeholders, transforming business requirements into technical solutions. Service mindedness, a white-glove-service approach, communication skills, and pro-activity are key skills required for the right candidate.
This position is on the Red Bull North America HQ Talent team and will be required to sit onsite in our North American Headquarters office, in Santa Monica, CA. We are in the office 4 days per week with the 5th business day being remote.
WHAT SUCCESS LOOKS LIKE
Pipelines run reliably with high data quality and minimal rework
Transformation models are clean, tested, and documented to team standards
AI-ready data layers are in place and accelerating intelligent analytics delivery on Snowflake Cortex
Business teams receive accurate, well-documented data products without needing to re-open requirements
All the responsibilities we'll trust you with:
Design, build, and maintain data pipelines using modern orchestration tools (e.g., Dagster, Airflow, or equivalent)
Develop and optimize Snowflake data models — including dynamic tables, streams, tasks, and materialized views — for performance and reliability
Ingest and process structured and semi-structured data (CSV, JSON, Parquet) via automated ELT workflows
Write Python for data manipulation, automation, and pipeline development — following engineering best practices including testing, documentation, and code optimization
Manage version control and collaboration through GitHub, adhering to branching strategies and code review standards
Build and maintain CI/CD pipelines to automate testing, validation, and deployment of data assets
Contribute to data lake design and maintenance, ensuring data integrity, lineage, and quality standards
Build clean, AI-ready data layers that support agentic analytics and intelligent querying use cases on Snowflake Cortex
Contribute to semantic layer development alongside senior engineers, supporting clean, consistent data access patterns for AI and analytics consumers
Support the team's work in AI for analytics on Snowflake Cortex — executing on agent-driven workflows and automated insight pipelines under the guidance of senior engineers
Monitor and troubleshoot pipelines to ensure uptime, data quality, and SLA compliance
Implement testing frameworks within your transformation layer to validate accuracy and catch issues early
Identify opportunities to optimize pipeline performance, reduce latency, and lower compute cost
Partner with business analysts and Sales, Distribution, Operations, and Finance teams to translate requirements into technical solutions
Engage business stakeholders with a service-first mindset — proactively communicating, setting clear expectations, and following through
Document pipeline designs, data flows, and technical decisions to support team knowledge and auditability
Build relationships with global data engineering teams to align on standards and shared solutions
Contribute to the Analytics roadmap for short, medium, and long-term business needs
Innovate and enhance our data lakes and data fabric, ensuring alignment with business goals
Stay current with industry trends and emerging technologies, particularly in the Snowflake ecosystem and AI-driven analytics
Own your work end-to-end — manage priorities, track commitments in Jira, and don't wait to be asked
Collaborate openly across engineering, analytics, and business teams in a high-trust, low-bureaucracy environment
Bring a white-glove mindset to business stakeholders — responsive, clear, and solutions-oriented
that matter most for this role:
In the 1980’s Dietrich Mateschitz developed a formula known as the Red Bull Energy Drink. This was not only the launch of a completely new product, in fact it was the birth of a totally new product category.
The company beyond the canChasing our potential
Since the early days of Red Bull, an entrepreneurial mindset has always guided our approach to work and the environment we create:
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