Data Engineer

Red Bull•Santa Monica, CA

20h•$112,000 - $168,000

About The Position

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. 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

Requirements

3+ years of experience in data engineering or analytics engineering
Bachelor's degree or higher in Computer Science, Information Systems, Data Engineering, or a related field.
Hands-on experience with a modern cloud data warehouse platform (e.g., Snowflake, Databricks, or equivalent): SQL, data modeling, and performance tuning
Working proficiency with a SQL-based data transformation framework (e.g., dbt or equivalent)
Experience with a workflow orchestration tool (e.g., Dagster, Airflow, Prefect, or equivalent)
Python proficiency: data manipulation, scripting, pipeline development, and Git-based version control
Experience with GitHub and CI/CD pipeline tooling for data asset deployment
Familiarity with cloud storage and compute services (e.g., AWS, Azure, or GCP)
Strong communication skills; proactive, collaborative, and service-minded
Demonstrated ability to work effectively with business users at all levels
High level of responsibility and accountability, with a commitment to delivering high-quality solutions
Willingness to travel as needed for onboarding and collaboration with global teams
Fluent in English; additional language skills an advantage.

Nice To Haves

Experience with agentic AI frameworks like Snowflake Cortex or equivalent is a strong plus

Responsibilities

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