Senior Data Operations Engineer – DataOps
Job Description:
• Lead the design and implementation of enterprise-scale DataOps platforms and automation frameworks.
• Architect and evolve GCP-native data platforms supporting high-throughput batch and real-time workloads.
• Design and implement microservices-based data architectures using containerization technologies.
• Build and maintain CI/CD pipelines for data workflows, including automated testing and deployment.
• Develop Infrastructure as Code (IaC) solutions to standardize and automate platform provisioning.
• Implement robust data orchestration, monitoring, and observability capabilities.
• Establish and enforce data quality frameworks to ensure reliability and trust in data products.
• Support real-time data platforms operating at extreme scale.
• Partner with platform squads to deliver self-service data infrastructure products.
• Drive best practices for automation, resiliency, scalability, and operational excellence.
• Influence technical direction, mentor senior engineers, and lead through ambiguity.
Requirements:
• 8+ years of progressive experience in DataOps, Data Engineering, or Platform Engineering roles.
• Strong expertise in data warehousing, data lakes, and distributed processing technologies (Spark, Hadoop, Kafka).
• Advanced proficiency in SQL and Python; working knowledge of Java or Scala.
• Deep experience with Google Cloud Platform (GCP) data and infrastructure services.
• Expert understanding of microservices architecture and containerization (Docker, Kubernetes).
• Proven hands-on experience with Infrastructure as Code tools (Terraform preferred).
• Strong background in CI/CD methodologies applied to data pipelines.
• Experience designing and implementing data automation frameworks.
• Advanced knowledge of data orchestration, monitoring, and observability tooling.
• Ability to architect highly scalable, resilient, and fault-tolerant data systems.
• Strong problem-solving skills and ability to operate independently in ambiguous environments.
Benefits:
• Flexible work arrangements
• Professional development opportunities
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• Lead the design and implementation of enterprise-scale DataOps platforms and automation frameworks.
• Architect and evolve GCP-native data platforms supporting high-throughput batch and real-time workloads.
• Design and implement microservices-based data architectures using containerization technologies.
• Build and maintain CI/CD pipelines for data workflows, including automated testing and deployment.
• Develop Infrastructure as Code (IaC) solutions to standardize and automate platform provisioning.
• Implement robust data orchestration, monitoring, and observability capabilities.
• Establish and enforce data quality frameworks to ensure reliability and trust in data products.
• Support real-time data platforms operating at extreme scale.
• Partner with platform squads to deliver self-service data infrastructure products.
• Drive best practices for automation, resiliency, scalability, and operational excellence.
• Influence technical direction, mentor senior engineers, and lead through ambiguity.
Requirements:
• 8+ years of progressive experience in DataOps, Data Engineering, or Platform Engineering roles.
• Strong expertise in data warehousing, data lakes, and distributed processing technologies (Spark, Hadoop, Kafka).
• Advanced proficiency in SQL and Python; working knowledge of Java or Scala.
• Deep experience with Google Cloud Platform (GCP) data and infrastructure services.
• Expert understanding of microservices architecture and containerization (Docker, Kubernetes).
• Proven hands-on experience with Infrastructure as Code tools (Terraform preferred).
• Strong background in CI/CD methodologies applied to data pipelines.
• Experience designing and implementing data automation frameworks.
• Advanced knowledge of data orchestration, monitoring, and observability tooling.
• Ability to architect highly scalable, resilient, and fault-tolerant data systems.
• Strong problem-solving skills and ability to operate independently in ambiguous environments.
Benefits:
• Flexible work arrangements
• Professional development opportunities
Apply tot his job
Apply To this Job