Engineering Manager, Data Platform & Ml Ops

Jobgether; México; Remote; MXN 180,000 - 260,000; Full time

Benefits offered by this job

Remote-friendly

Job summary

Jobgether está buscando un Engineering Manager para Data Platform & ML Ops en México, remoto. Liderarás equipos en plataformas de datos y operaciones de ML, guiando arquitectura y mejores prácticas para entregar soluciones escalables y confiables.

El rol combina liderazgo técnico, gestión de personas y toma de decisiones estratégicas, trabajando con data scientists, PMs y stakeholders para impulsar impacto y crecimiento del negocio.

Qualifications

• Experiencia en gestión de equipos de datos y ML.
• 5+ años en data/ML o software.
• Capacidad de dirigir arquitectura técnica.
• Experiencia en SaaS y entornos de nube.
• Capacidad de entregar productos de datos confiables.

Responsibilities

• Liderar y orientar a un equipo de ingenieros en plataformas de datos y ML Ops.
• Encabezar la fiabilidad y escalabilidad de la infraestructura de datos.
• Gestionar el ciclo de vida ML: experimentación, pipelines y monitoreo.
• Brindar guía técnica en discusiones de arquitectura.
• Colaborar con científicos de datos, PMs, analistas y líderes.
• Establecer estándares y buenas prácticas en datos y ML.
• Apoyar el desarrollo del equipo mediante coaching y crecimiento.
• Impulsar la innovación en un entorno tecnológico de rápido movimiento.

Skills

Data platforms

ML engineering

Leadership

Cloud environments

Data infrastructure

SaaS experience

Tools

ClickHouse

Databricks

Snowflake

BigQuery

AWS

Job description

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Engineering Manager, Data Platform & ML Ops

Empresa : Jobgether Tipo de empleo : Tiempo completo Remote / Work from Home JobCopilot will tailor your resume and auto-fill the application

Descripción del trabajo - Engineering Manager, Data Platform & ML Ops

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an Engineering Manager, Data Platform & ML Ops based in Mexico.

This role offers the opportunity to lead a high-performing engineering team building the foundation for advanced data and machine learning capabilities. You will oversee critical platforms that power analytics, intelligent products, and scalable ML operations. The position combines technical leadership, people management, and strategic decision‑making in a fast‑moving environment. You will guide engineers, influence architecture, and establish best practices across data infrastructure and ML systems. Working closely with cross‑functional teams, you will transform complex data challenges into impactful solutions. This is an ideal opportunity for an experienced engineering leader passionate about innovation, reliability, and team growth.

Accountabilities:

As an Engineering Manager, you will lead the development and evolution of data platforms and ML operations capabilities while supporting engineering excellence and business impact. You will be responsible for building strong teams, driving technical strategy, and ensuring reliable systems that enable data‑driven products.

• Lead and mentor a team of engineers working across data platforms and machine learning operations.
• Own the reliability, scalability, and continuous improvement of internal data infrastructure supporting analytics and product initiatives.
• Oversee the complete ML lifecycle, including experimentation, training pipelines, model deployment, and production monitoring.
• Provide technical guidance by contributing to architecture discussions, reviewing solutions, and helping teams make effective engineering decisions.
• Collaborate with data scientists, product managers, analysts, and engineering leaders to turn data and ML investments into measurable outcomes.
• Establish engineering standards, processes, and best practices across data engineering and ML operations.
• Support team development through coaching, feedback, knowledge sharing, and career growth opportunities.
• Drive innovation and continuous improvement within a rapidly evolving technical environment.

Requirements:

The ideal candidate is an experienced engineering leader with a strong background in data engineering, ML engineering, or related software engineering disciplines. You should combine technical expertise with proven people leadership skills and the ability to deliver scalable, reliable solutions.

• At least 2 years of experience managing engineering teams focused on data platforms, machine learning, or related technologies.
• 5+ years of professional experience in data engineering, ML engineering, or software engineering roles, preferably within SaaS environments.
• Strong understanding of both data infrastructure and machine learning systems, with the ability to provide technical direction across both areas.
• Experience leading engineers across multiple technical disciplines and supporting high-performing teams.
• Proven ability to deliver reliable data products and platforms with a focus on quality, scalability, and user impact.
• Experience driving technical change and innovation in fast‑paced, growing organizations.
• Familiarity with analytical storage technologies such as ClickHouse, Databricks, Snowflake, or BigQuery.
• Experience with ML lifecycle tools, including training pipelines, model serving, and production monitoring.
• Knowledge of cloud‑based data and ML infrastructure, particularly AWS environments.
• Strong communication, collaboration, and stakeholder management skills.

Benefits:

• Remote‑friendly work environment with flexibility to work from your preferred location.
• Competitive compensation package aligned with experience, skills, and market conditions.
• Opportunity to work on advanced data and machine learning systems at significant scale.
• Chance to lead and grow a talented engineering team while shaping technical strategy.
• Exposure to modern technologies across data platforms, ML operations, and cloud infrastructure.
• Inclusive workplace culture that values diverse perspectives and backgrounds.
• Professional growth opportunities through continuous learning and technical challenges.
• Opportunity to contribute to innovative products focused on solving complex real‑world problems.