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 Open. Shift, 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 .

Responsibilities

Design, build and maintain scalable data pipelines and platform components using Snowflake, Apache Spark (Py. Spark), SQL and Apache Airflow Deploy, operate and support data and ML workloads on Kubernetes and Open. Shift 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 Requirements Proven experience in data engineering and enterprise-scale data platform delivery Hands-on expertise with Snowflake, Apache Spark (Py.

Spark), SQL and Apache Airflow Strong Python development capability, including building services or APIs Production experience operating workloads on Kubernetes or Red Hat Open. Shift 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 Nice to have 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)