Note: The job is a remote job and is open to candidates in USA. Stord is a commerce-enablement technology and fulfillment company serving brands through technology and high-volume fulfillment services. The Senior Data Engineer will design, build, and maintain GCP-based data pipelines and infrastructure that transform operational data into reliable, governed, ML-ready data, while ensuring performance, security, and reliability and mentoring the data team.
Responsibilities
- Design and build scalable data pipelines using GCP services like Cloud Dataflow, Cloud Pub/Sub, Data Stream, Spark, and Cloud Composer, ingesting, processing, and storing large datasets from multiple sources
- Develop, test, and maintain data models, schemas, and ETL (Extract, Transform, Load) processes using BigQuery, Cloud SQL, Spark, and Data Studio
- Partner with stakeholders to understand business requirements and translate them into data infrastructure that actually solves the problem
- Optimize data pipelines for performance, scalability, and cost efficiency, using GCP-native tools such as Dataproc and Bigtable
- Ensure data quality, integrity, and security by building validation processes and enforcing data governance, access control, and security best practices
- Automate workflows using Cloud Composer so pipelines run reliably and on schedule, not by accident
- Own pipeline and data infrastructure reliability end-to-end — monitoring, alerting, troubleshooting, and root cause analysis — partnering with SRE and Infrastructure on shared GCP resources (compute, storage, network) rather than handing reliability off to someone else
- Prepare, model, and expose data for ML and agentic AI use cases, including the governed data pipelines Stord's AI tooling queries in production
- Mentor the broader data team as a senior technical voice on pipeline design and data infrastructure standards
- Stay ahead of GCP's roadmap and emerging best practices, and put what you learn back into the platform
Skills
- 5+ years of data engineering experience, including at least three years in a cloud environment (preferably GCP)
- Expertise across GCP services: BigQuery, Dataflow, Pub/Sub, Cloud Composer, Cloud Storage, Cloud Functions, and Cloud SQL
- Strong SQL, with real experience writing complex queries for data extraction and analysis
- Hands-on ETL development and workflow orchestration using tools like Apache Airflow
- Proficiency in Python and/or Spark for data processing and pipeline development
- Experience with streaming data pipelines (Pub/Sub, Dataflow, or similar) as well as batch processing
- A solid grasp of data warehousing concepts, large datasets, and query optimization
- Working knowledge of data governance, security, and compliance in a cloud environment
- Understanding of ML concepts and data preparation for ML applications, with interest in agentic AI tooling a must
- Strong analytical, problem-solving, and troubleshooting instincts, with real attention to detail
- Clear, confident communication and the ability to collaborate across teams
- Bachelor's degree in computer science or a comparable field, or equivalent experience
- GCP certification (Professional Data Engineer or Cloud Architect)
- Experience with ML infrastructure on GCP, such as AI Platform and TensorFlow
- Familiarity with DevOps/MLOps practices — CI/CD, infrastructure as code (Terraform), and containerization (Kubernetes, Docker) on GCP
- Experience with other data processing frameworks, such as Apache Spark
- A background in logistics, supply chain, 3PL, or warehouse/fulfillment technology
- Experience working on a distributed team
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