Senior Data Engineer

• 5+ years of Data Engineering experience, including strong expertise across GCP technologies such as BigQuery, Dataflow, Pub/Sub, Cloud Composer, and Cloud SQL.
• Hands-on experience with SQL, Python, Spark, ETL/ELT development, batch processing, and streaming architectures.
• Strong understanding of data warehousing, governance, security, and preparing data for machine learning applications.

Preferred Experience

• Experience building and scaling large data platforms within high-growth startups or enterprise-scale environments.
• Background supporting AI/ML initiatives and modern cloud infrastructure using Terraform, Kubernetes, Docker, or CI/CD tools.
• Industry experience in logistics, supply chain, fulfillment, or transportation technology and a track record of technical leadership.

Additional Information

In addition to base pay, direct-hire employees may be eligible for client-offered benefits such as medical, dental, and vision coverage, bonus opportunities, equity participation, and paid leave where required by applicable law. Eligibility may vary based on factors such as location and hire date and is subject to change., In addition to base pay, direct-hire employees may be eligible for client offered benefits such as medical, dental, and vision coverage, and paid leave where required by applicable law. Eligibility may vary based on factors such as location and hire date and is subject to change.

Our client is transforming the logistics and supply chain industry through technology, data, and AI. Their platform powers more than $10 billion in annual commerce volume across fulfillment, warehousing, and supply chain operations. As a Senior Data Engineer, you’ll help build the data infrastructure that drives analytics, machine learning, and business decision-making. This is an opportunity to join a high-growth company where data plays a critical role in product innovation and operational success.

What You’ll Do

• Design and build scalable data pipelines and cloud-native infrastructure across the GCP ecosystem.
• Develop ETL processes, data models, and streaming solutions that support analytics and AI initiatives.
• Drive platform reliability, data quality, and engineering best practices while partnering across technical teams.