Senior DataOps Engineer

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Senior DataOps Engineer in United States.

This role sits at the intersection of DevOps and data engineering, focusing on building, operating, and scaling the infrastructure that powers modern data platforms and analytics workloads. You will design and maintain robust cloud environments that support large-scale distributed data processing, while also ensuring the reliability and performance of production data pipelines. Working closely with engineering teams, you will help automate deployments, strengthen CI/CD workflows, and operationalize Spark-based ecosystems across cloud providers. This position is highly hands-on, blending infrastructure engineering with real data pipeline ownership. You will play a key role in ensuring that data systems are secure, efficient, and production-ready at scale. The environment is collaborative, fast-moving, and centered on automation, reliability, and continuous improvement.

• Build, maintain, and optimize CI/CD pipelines and infrastructure-as-code frameworks supporting data platforms and services.
• Provision, configure, and operate cloud-based data processing environments, including Spark clusters and orchestration tools such as Airflow, Databricks, or EMR.
• Develop, test, and maintain data pipelines running on production infrastructure, ensuring reliability from development through deployment and monitoring.
• Design scalable and secure cloud infrastructure templates and automation across AWS, Azure, GCP, or OCI environments.
• Investigate, troubleshoot, and resolve data pipeline, integration, and platform issues, providing root-cause analysis and long-term fixes.
• Monitor system performance, manage incidents, optimize workloads, and control cloud infrastructure costs.
• Promote DataOps and “Everything-as-Code” best practices across engineering teams and workflows.
• Support engineers and developers with deployment processes, builds, and operational improvements.

Requirements:

• 6+ years of experience in DevOps, DataOps, or data platform engineering roles.
• Strong hands-on experience with Apache Spark and managed Spark platforms such as Databricks, AWS EMR, or GCP Dataproc.
• Proficiency in Python, with solid knowledge of Java or Scala for data-intensive applications.
• Experience with orchestration tools such as Apache Airflow, Prefect, or similar platforms.
• Strong background in CI/CD tooling such as GitHub Actions, GitLab CI, or Jenkins.
• Hands-on expertise with infrastructure-as-code tools like Terraform or Ansible (CloudFormation or ARM Templates is a plus).
• Solid experience working with Linux systems and cloud environments (AWS, Azure, GCP, or OCI).
• Familiarity with Kubernetes or containerized data workloads in production environments.
• Strong ability to manage multiple priorities in parallel while maintaining delivery quality.
• Excellent communication skills with a customer-focused and collaborative mindset.

Benefits:

• Competitive annual salary ranging from $130,000 to $160,000.
• Remote-first work environment with flexibility in how and where you work.
• Comprehensive health, medical, life, and disability coverage.
• Flexible time-off policy including PTO, personal days, and sick leave.
• Retirement savings programs (RRSP, TFSA, or 401K employee contributions).
• Employee Assistance Program supporting wellbeing and mental health.
• Continuous learning opportunities, including FHIR-focused training and Skillsoft learning resources.
• Inclusive and diverse workplace culture that values authenticity and belonging.