DataOps Engineer Lead

Spin; México; Presencial; MXN 167.400 - 245.520; Jornada completa

Descripción de la vacante

An innovative company is seeking a DataOps Engineering Lead to spearhead a dynamic team focused on building reliable and scalable data pipelines. This role combines technical leadership with hands-on expertise in tools like Databricks on AWS, ensuring seamless integration across various cloud environments. You'll foster a culture of accountability and continuous improvement while collaborating with cross-functional teams to drive the company's data strategy. If you thrive in a fast-paced environment and are passionate about data operations, this opportunity is perfect for you.

Formación

• 7+ years in DataOps or DevOps, with leadership experience.
• Advanced experience with Databricks and AWS services.
• Proficient in Python and SQL for automation.

Responsabilidades

• Lead the DataOps team and define CI/CD pipelines.
• Ensure observability and alignment with other technical teams.
• Promote infrastructure as code practices across cloud environments.

Conocimientos

DataOps

DevOps

Leadership

Python

SQL

Cloud Platforms

CI/CD Pipelines

Monitoring

Problem-solving

Educación

Bachelor's Degree in Computer Science or related field

Herramientas

Databricks

AWS

GitHub Actions

Terraform

Airflow

Docker

Kubernetes

Descripción del empleo

2 days ago Be among the first 25 applicants

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 environment that aligns with the company's values and culture.

Required Knowledge and Experience

• 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.
• Preferred Qualifications:
• Experience with microservices architecture and containerization technologies.
• Familiarity with ITIL or other IT service management frameworks.
• Certification in cloud platforms or DevOps practices.
• Experience working with Google Cloud Platform (GCP) services such as BigQuery, Cloud Functions, Pub/Sub, or Composer.

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Information Technology

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