2 days ago Be among the first 25 applicants Objective of the Role The
Data Ops Engineering Lead
is responsible for leading the Data Ops 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 Data Ops 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 Git Hub Actions, Terraform, and Airflow. Oversee observability standards: logging, monitoring, alerting, and retries across the entire pipeline lifecycle. Ensure alignment between Data Ops 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 (Ia C) 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 Data Ops 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. Required Knowledge and Experience Minimum 7 years in Data Ops, or Dev Ops, 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
(Git Hub Actions, Git Lab 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 Dev Ops 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. Preferred Qualifications: Experience with microservices architecture and containerization technologies. Familiarity with ITIL or other IT service management frameworks. Certification in cloud platforms or Dev Ops practices. Experience working with Google Cloud Platform (GCP) services such as Big Query, Cloud Functions, Pub/Sub, or Composer. Seniority level
Seniority level Mid-Senior level Employment type
Employment type Full-time Job function
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📌 Dataops engineer lead (Ciudad de México)
🏢 Spin
📍 Ciudad de México