As a Manager - DataOps, you are responsible for coordinating activities of staff engaged in business systems, computer operations, computer systems, computer programming, and company's network to assure effective computer resources are provided to users.

Responsibilities:

Lead and mentor the DataOps Engineering team, fostering a culture of accountability, continuous improvement, and technical excellence. Define and implement CI/CD pipelines and automation practices using tools such as GitHub Actions, Terraform, and Airflow. Oversee observability standards: logging, monitoring, alerting, and retries across the entire pipeline lifecycle. Ensure alignment between DataOps and other technical chapters (Engineering, Platform, Architecture, Security) to support cross-domain pipelines. Collaborate with business stakeholders and tech leads to proactively manage delivery plans, risks, and dependencies. Act as the technical authority for incident response, root cause analysis, and resilience strategies in production environments. Promote infrastructure as code (IaC) practices and drive automation across cloud environments. Monitor resource usage and optimize cloud costs (Databricks clusters, compute, storage). Facilitate team rituals (1:1s, planning, retros) and create career development opportunities for team members. Represent the DataOps function in planning, roadmap definition, and architectural discussions. Promote an autonomous work culture by encouraging self-management, accountability, and proactive problem-solving among team members. Serve as a Spin Culture Ambassador to foster and maintain a positive, inclusive, and dynamic work environment that aligns with the company's values and culture.

Qualifications:

Minimum 7 years in DataOps, or DevOps, with at least 1-2 years in a technical leadership role overseeing and mentoring Data Engineers. Demonstrates experience in managing complex projects, coordinating team efforts, and ensuring alignment with organizational goals. Advanced hands-on experience with Databricks, including Unity Catalog, Delta Live Tables, Job orchestration, and monitoring. Solid experience in cloud platforms, especially AWS (S3, EC2, IAM, Glue). Experience with CI/CD pipelines (GitHub Actions, GitLab CI), and orchestration frameworks (Airflow or similar). Proficient in Python, SQL, and scripting for automation and data operations. Strong understanding of data pipeline architectures across batch, streaming, and real-time use cases. Technical Skills: Proficiency in DevOps tools and technologies such as Jenkins, Docker, Kubernetes, Terraform, Ansible, and cloud platforms (e.g. Databricks, AWS, Azure, GCP). Soft Skills: Strong leadership, communication, and collaboration skills. Excellent problem-solving abilities and a proactive approach to learning and innovation. Experience implementing monitoring and data quality checks (e.g., Great Expectations, Datadog, Prometheus). Effective communicator who can bridge technical and business needs.