Data Engineer

This Jobot Job is hosted by: Amanda Preston
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Salary: $155,000 - $175,000 per year

A bit about us:

We are a global organization operating at the intersection of technology, data, and digital platforms, supporting products and services used by millions worldwide. Our teams build and operate highly scalable, cloud-native systems that power data-driven decision-making, advanced analytics, and machine learning across a diverse and complex ecosystem. With a strong emphasis on modern engineering practices, automation, and innovation, we invest heavily in the people and platforms that enable long-term growth and technical excellence.

Why join us?

Work at scale: Build and operate production systems that support large, globally distributed platforms and high-impact use cases.

Technical ownership: Play a key role in shaping MLOps, DevOps, and cloud architecture standards across the organization.

Modern stack: Work with Kubernetes, Google Cloud Platform, Terraform, CI/CD, and production machine learning systems.

Real-world impact: Deploy and operationalize machine learning models that directly support business-critical workflows.

Collaborative culture: Partner closely with experienced engineers, data scientists, and product leaders who value strong technical judgment.

Growth and stability: Join a mature, well-funded environment that offers long-term career growth and technical depth without startup volatility.

Job Details

Role Summary

Join an elite engineering team at a large, globally distributed organization building next-generation, data-driven platforms at scale. We are seeking a highly motivated and skilled Senior MLOps/DevOps Engineer to lead the architecture, deployment, and operation of modern machine learning and cloud-native systems.

This role is critical in bridging Data Science and Platform Operations, ensuring machine learning models and core services are deployed reliably, scalably, and securely in production cloud environments. The position requires deep expertise in automation, cloud infrastructure, and the full machine learning lifecycle.

Key Responsibilities

MLOps Pipeline Ownership: Design, implement, and manage end-to-end MLOps pipelines for continuous training, deployment, monitoring, and versioning of production ML models (e.g., recommendation systems, content intelligence, or analytics platforms).

Data Pipeline Design & Orchestration: Design, build, and maintain robust data ingestion and transformation pipelines using modern orchestration tools such as Dagster or Airflow.

Kubernetes & Cloud Architecture: Architect, deploy, and maintain highly scalable, fault-tolerant infrastructure using Kubernetes (GKE) within Google Cloud Platform (Google Cloud Platform).

DevOps & Automation: Champion DevOps best practices, including Infrastructure as Code (IaC) using Terraform or similar tools, and establish reliable CI/CD workflows.

CI/CD Implementation: Configure and manage automated deployment and testing pipelines using GitHub Actions and related tooling to ensure fast, reliable releases.

Core Development: Write clean, efficient, and well-tested Python code for automation, infrastructure tooling, and service integration.

API & Service Deployment: Design, develop, and deploy high-performance Python APIs (FastAPI, Flask, or similar) to serve ML predictions and application services in production.

Monitoring & Observability: Implement comprehensive monitoring, logging, and alerting solutions (e.g., Prometheus, Grafana, cloud-native logging tools) to maintain system reliability.

Security & Compliance: Implement security best practices, access controls, and compliance requirements appropriate for large-scale, enterprise environments.

Collaboration & Mentorship: Partner closely with data scientists, software engineers, and product teams while providing technical guidance and mentorship to junior engineers.

Required Qualifications

5+ years of experience in DevOps, Cloud Engineering, or related roles, including 2+ years focused on MLOps in production environments

Advanced proficiency in Python for development, automation, and scripting

Proven experience building and deploying production-grade APIs and backend services (FastAPI, Flask, Django)

Strong SQL skills and experience designing and optimizing data models for relational and NoSQL systems (e.g., BigQuery, Cloud SQL)

Hands-on experience with workflow orchestration tools (Dagster, Airflow)

Expert knowledge of Docker and Kubernetes, including Helm and Kubernetes-native IaC tools (e.g., Crossplane); GKE experience strongly preferred

Extensive experience within the Google Cloud Platform (Google Cloud Platform) ecosystem (Compute Engine, Cloud Storage, BigQuery, Pub/Sub, Vertex AI)

Strong GitHub-based development workflows (branching, pull requests, code reviews)

Solid understanding of network architecture, security principles, and large-scale data processing

Excellent communication, collaboration, and problem-solving skills

Interested in hearing more? Easy Apply now by clicking the "Apply Now" button.

Jobot is an Equal Opportunity Employer. We provide an inclusive work environment that celebrates diversity and all qualified candidates receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, age (40 and over), disability, military status, genetic information or any other basis protected by applicable federal, state, or local laws. Jobot also prohibits harassment of applicants or employees based on any of these protected categories. It is Jobot's policy to comply with all applicable federal, state and local laws respecting consideration of unemployment status in making hiring decisions.

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