Data Engineer

Stellar Consulting Solutions, LLC is seeking an experienced Data Engineer to join their Supply Chain Data & AI Organization. This role involves designing, developing, and delivering enterprise data products and analytics solutions, with a focus on building cloud-native data platforms on Google Cloud Platform (GCP). The ideal candidate will collaborate with various stakeholders to create scalable data solutions that enhance analytics and decision-making.

Responsibilities:

• Design, develop, and implement scalable data pipelines and data products on Google Cloud Platform (GCP)
• Build and optimize enterprise data solutions using Dataproc, BigQuery, SQL, and dbt
• Design robust and scalable data models that support analytical and operational reporting requirements
• Develop efficient ETL/ELT pipelines to ingest, transform, and publish data from multiple enterprise systems
• Collaborate with Product Managers, Business Analysts, Enterprise Solution Architects, Data Architects, and business stakeholders to translate business requirements into scalable technical solutions
• Lead technical design discussions and perform code reviews to ensure engineering quality and adherence to standards
• Optimize data processing performance, reliability, scalability, and cost across cloud-based data platforms
• Implement monitoring, testing, and operational best practices to support production workloads
• Contribute to reusable frameworks, engineering standards, and documentation that improve team productivity and solution consistency
• Support production issue resolution and continuous improvement initiatives
• Work effectively within Agile delivery teams and participate in sprint planning, estimation, and backlog refinement

Requirements:

• 4+ years of experience in Data Engineering with demonstrated technical leadership on enterprise data projects
• Strong hands-on experience with Google Cloud Platform (GCP)
• Expert-level proficiency in Dataproc, BigQuery, SQL, dbt (Data Build Tool)
• Strong understanding of modern ETL/ELT architecture and large-scale data processing
• Strong knowledge of data modeling techniques, including dimensional modeling, normalized data models, and analytical data warehouse design
• Experience building scalable and maintainable cloud-native data pipelines
• Experience with Git, CI/CD pipelines, and engineering best practices
• Strong analytical, troubleshooting, and problem-solving skills
• Experience with Apache Airflow for workflow orchestration
• Experience integrating enterprise data platforms with Apache Kafka or other streaming technologies
• Working knowledge of PySpark for distributed data processing
• Proficiency in Python for data engineering, automation, and utility development
• Familiarity with data quality, metadata management, and data governance best practices