Senior Data Engineer
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Job details:
Join a World Class Team Operating at the Intersection of Technology, Data, and Digital Products
This Jobot Job is hosted by: Amanda Preston
Are you a fit? Easy Apply now by clicking the "Easy Apply" button
and sending us your resume.
Salary: $155,000 - $175,000 per year
A bit about us:
Competitive salary and comprehensive benefits
Long-term stability with continued investment in technology and engineering
High-visibility work with real-world impact
A collaborative, engineering-driven culture focused on quality and continuous improvement
Why join us?
Work on data and machine learning platforms operating at significant scale
Own and influence core systems that power critical business capabilities
Collaborate with experienced engineers and data scientists in a highly technical environment
Tackle complex engineering challenges with modern cloud and MLOps tooling
Enjoy the stability of a mature organization combined with the opportunity to modernize and innovate
Job Details
Senior Data Engineer
Role Summary
Join a high-performing engineering team at a large, global organization operating at the intersection of technology, data, and digital products. We are seeking a highly motivated Senior Data Engineer to lead the architecture, deployment, and operation of next-generation, data-driven platforms.
In this role, you will bridge the gap between Data Science and Production Engineering, ensuring that datasets, machine learning models, and core services are deployed reliably, scalably, and securely in the cloud. This is a high-impact position requiring deep expertise in data architecture, backend engineering, and the full machine learning lifecycle in production environments.
Key Responsibilities
Data Pipeline Design & Orchestration
Design, build, and maintain robust data ingestion and transformation pipelines
Leverage modern orchestration tools to ensure reliable, observable data flows supporting machine learning workloads
Core Development
Write clean, efficient, and well-tested Python code for automation, infrastructure tooling, and service integration
Develop shared libraries and glue services connecting cloud-native components
API & Service Deployment
Design, develop, and deploy high-performance Python APIs (FastAPI / Flask) to serve machine learning predictions and core application logic
MLOps Pipeline Ownership
Own end-to-end pipelines for continuous training, deployment, versioning, and monitoring of production ML models (e.g., recommendation or personalization systems)
Infrastructure Management
Architect and maintain scalable, fault-tolerant infrastructure using Kubernetes (GKE) within Google Cloud Platform
Ensure reliability, performance, and cost efficiency across environments
Collaboration & Mentorship
Partner closely with data scientists, software engineers, and platform teams
Provide technical leadership and mentorship to junior engineers
Qualifications
Must-Have (Engineering Excellence)
5+ years of professional experience in Data Engineering, Software Engineering, or Cloud Engineering
Deep expertise in Python for application development, data processing, and automation
Proven experience building and deploying production-grade backend services and APIs (FastAPI, Flask, or Django)
Strong SQL skills with experience designing and optimizing schemas for relational and analytical data stores (e.g., BigQuery, Cloud SQL)
Hands-on experience with data orchestration tools such as Dagster or Airflow
Extensive experience designing and operating services within Google Cloud Platform (BigQuery, Pub/Sub, Vertex AI, Compute Engine)
Expert-level knowledge of Docker and Kubernetes, including Helm-based deployments
Nice-to-Have (DevOps & MLOps)
Experience with Infrastructure as Code tools such as Terraform or Crossplane
CI/CD experience using GitHub Actions or similar tooling
Familiarity with observability stacks (Prometheus, Grafana, Cloud Logging)
Understanding of cloud security principles and enterprise compliance requirements
Direct experience supporting production MLOps workflows (model monitoring, drift detection, automated retraining)
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