DataOps Engineer

Team Overview

As part of our high-impact DevOps team, you will enable and empower Data Platform, ML Platform, and AI Product teams by providing world-class infrastructure, automation, and operational reliability. You will partner with leading technologists in data engineering, machine learning, and AI product development to build and operate robust, compliant, and scalable data and ML pipelines that underpin Tekions products and analytics.

Roles and Responsibilities

Infrastructure Enablement: Design, provision, and optimize infrastructure required for data and ML platforms (on-premises and cloud), including orchestration, storage, compute, and security.

Automation: Build and maintain robust automation for deployment, configuration management, monitoring, scaling, and self-healing of data and ML pipelines.

• Platform Operations: Ensure the reliability, performance, and availability of data processing and ML serving environments using best practices in DataOps and MLOps.
• Collaboration: Work closely with Data Platform, ML Platform, and AI Product teams to understand evolving requirements and translate them into scalable platform capabilities.
• Observability & Monitoring: Implement end-to-end observability for critical data flows and ML jobs using appropriate logging, monitoring, and alerting systems.
• CI/CD for Data & ML: Develop CI/CD pipelines tailored for data workflows and machine learning models, ensuring continuous integration, automated testing, validation, and deployment.
• Security & Compliance: Support and enforce security, governance, and compliance requirements for sensitive data and regulated workflows.
• Continuous Improvement: Drive innovation by adopting new data and ML infrastructure technologies and improving operational practices for agility, scalability, and reliability.
• Tech Stack Ownership: Manage, tune, and upgrade a range of data and ML stack technologies, including (but not limited to):
• Apache Spark, Apache Airflow, Apache Kafka, Presto/Trino, Apache Hive
• Kubernetes, Docker, Argo Workflows, Kubeflow, MLflow, TensorFlow Serving, TorchServe
• Databricks, AWS/GCP/Azure Data Services, S3, Redshift, EMR
• Terraform, Ansible, Jenkins, GitHub Actions

What makes you relevant for this role?

Bachelors or masters degree in computer science, Engineering, or a related technical field, or equivalent work experience.

3+ years of experience in DataOps, MLOps, DevOps, Data Engineering, or related roles supporting complex data and ML platforms.

Strong hands-on skills with two or more in the following: Apache Spark, Apache Airflow, Kafka, Presto/Trino, Hive, Databricks or equivalent.

• Experience with containerization and orchestration (Kubernetes, Docker) and infrastructure-as-code (Terraform, Pulumi).
• Solid understanding of cloud data platforms (AWS/GCP/Azure) and experience deploying/operating data and ML workloads on cloud.
• Proficiency in automation/scripting languages (Python, Go, etc.) for pipelines and operational tasks.
• Experience in building and managing CI/CD pipelines for data engineering or ML workflows.
• Strong troubleshooting skills and a passion for automation, reliability, scalability, and continuous improvement.