Data Engineer

Company Description

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

Job Description

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

Qualifications

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 mode