Lead Data Engineer

We are seeking a Lead Data Engineer (Python Kubernetes Snowflake) to own reliable data pipelines and production platforms across Cloud and on-prem. You will deploy and run data and ML workloads on OpenShift improve CI/CD troubleshoot complex issues and guide engineering teams as we scale an enterprise analytics and ML platform.

Apply today and be eligible for a one-month sign-on bonus.*

*Terms & Conditions apply.

• Design build and maintain scalable data pipelines and platform components using Snowflake Apache Spark (PySpark) SQL and Apache Airflow
• Deploy operate and support data and ML workloads on Kubernetes and OpenShift in production
• Develop and maintain Python services and APIs using FastAPI or similar frameworks
• Monitor troubleshoot and optimize performance reliability and data quality across pipelines and platforms
• Build and improve continuous integration and continuous delivery (CI/CD) pipelines for safe repeatable releases
• Partner with DevOps Platform ML infrastructure and application teams to deliver production-ready solutions
• Drive root cause analysis for complex incidents and define preventative fixes and operational best practices
• Provide technical leadership through architecture decisions reviews and mentoring

• Proven experience in data engineering and enterprise-scale data platform delivery
• Hands-on expertise with Snowflake Apache Spark (PySpark) SQL and Apache Airflow
• Strong Python development capability including building services or APIs
• Production experience operating workloads on Kubernetes or Red Hat OpenShift
• Solid background in continuous integration and continuous delivery (CI/CD) Git workflows containers and deployment automation
• Experience supporting solutions across Microsoft Azure and hybrid cloud environments
• Strength in monitoring troubleshooting and production support for distributed systems
• Track record of technical ownership clear communication and mentoring within engineering teams

• Experience with FastAPI Databricks or Splunk
• Exposure to MLOps practices ML platform operations or LLM and GenAI enablement platforms
• Infrastructure as Code experience (tooling aligned to your environment)