Senior Data Operations Engineer – DataOps
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Job Description
Lead the evolution of DataOps practices at a global scale. Design automated, resilient, scalable data platforms on GCP for self-service data infrastructure.
Responsibilities:
- 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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