Data Engineer

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

RESPONSIBILITIES
Areas that play to your strengths

All the responsibilities we’ll trust you with:
Expand all

• DATA ENGINEERING

  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

• AI & DATA INTELLIGENCE

  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

• QUALITY & CONTINUOUS IMPROVEMENT

  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

• COLLABORATION & STAKEHOLDER PARTNERSHIP

  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

• ROADMAP & INNOVATION

  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

• WAYS OF WORKING

  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

EXPERIENCE
Your areas of knowledge and expertise

that matter most for this role:

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

• Travel 0-10%
• Permanent
• Benefits eligible

WHERE YOU’LL BE BASED
Santa Monica California , United States

United States Red Bull North America