Principal Data Engineer

Breakthru Beverage Group•Chicago, IL

2d•Hybrid

About The Position

The Principal Data Engineer designs, develops, and optimizes scalable data pipelines, data products, and cloud-based solutions supporting enterprise business intelligence, advanced analytics, and AI capabilities. The role establishes data engineering standards and best practices, and drives reliable, secure, high-performing data solutions. As a senior individual contributor, the position provides technical leadership and architectural guidance, collaborates across functional teams, and mentors engineers in support of the organization’s Data & AI strategy. The position follows a flexible hybrid schedule: two days per week in the office and three days working from home. It can be performed from any Breakthru Beverage office location. The salary range is $110,000–$140,000, with a corporate bonus of 20%.

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related technical field.
At least 8 years of experience in Data Engineering, Software Engineering, or related technical roles.
Strong expertise in SQL and building scalable, complex data transformations.
Proficiency in Python for data engineering, orchestration, and automation workflows.
Hands-on experience designing and operating enterprise-grade data platforms on AWS and Snowflake.
Experience with dbt or similar modern ELT/transformation frameworks.
Proven experience building and optimizing production-grade batch and near-real-time data pipelines.
Strong understanding of dimensional modeling, curated data layers, and scalable data architecture patterns.
Experience designing data solutions that support analytics, AI/ML, and modern AI-driven applications.
Expertise with CI/CD, Git-based development workflows, automated testing, and infrastructure-as-code deployments.
Strong troubleshooting, problem-solving, and performance optimization skills.
Ability to work effectively in fast-paced, cross-functional enterprise environments.
Experience using AI-assisted engineering and developer productivity tools to improve code quality, reliability, and delivery speed.

Nice To Haves

Experience with data observability and data quality tools.
Knowledge of SAP data structures, including S/4 and BDC data products.
Experience in large-scale enterprise environments.

Responsibilities

Design, build, and maintain scalable data pipelines on AWS and Snowflake to ingest, transform, and serve enterprise data.
Develop ELT/ETL processes using dbt, SQL, and Python for BI, analytics, and AI use cases.
Design curated, scalable, AI-ready data models and data products for trusted enterprise consumption.
Optimize pipelines for performance, scalability, reliability, and cost efficiency.
Use AWS services such as S3, Lambda, Glue, DynamoDB, and MWAA, along with Snowflake, to build secure and resilient data solutions.
Implement ingestion frameworks for batch, API, CDC, streaming, and near-real-time processing.
Design data architectures that support modern AI and machine learning workloads.
Ensure workflow orchestration and management align with CI/CD and governance standards.
Embed data quality checks, testing frameworks, and validation controls into pipelines.
Support data lineage, metadata management, observability, and AI data governance initiatives.
Troubleshoot production data issues and implement sustainable root-cause solutions.
Partner with Data Governance to enforce enterprise data standards.
Implement CI/CD and Git-based development practices for data pipelines.
Monitor, debug, and improve production workflows and platform reliability.
Apply software engineering practices including code reviews, automated testing, and infrastructure-as-code principles.
Continuously improve operational excellence and platform scalability.
Partner with BI, Analytics, AI/ML, and business stakeholders to deliver scalable data and AI solutions.
Collaborate with AI and Data Science teams to enable model development and AI deployment.
Mentor engineers and contribute to architecture standards and engineering best practices.
Influence technical direction and communicate complex concepts to technical and business audiences.