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Objective of the Role
The DataOps Engineering Lead is responsible for leading the DataOps team and setting the technical and operational standards that ensure data pipelines are reliable, automated, observable, and scalable. This role blends deep technical expertise with team leadership and cross-functional collaboration. Working primarily with Databricks on AWS, and optionally integrating with GCP environments, you'll help build a platform that powers analytics, business intelligence, and AI use cases across the company.
Main 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
📌 DataOps Engineer Lead (México)
🏢 Spin
📍 México