3 publicaciones de empleo destacadas de machine learning platform engineer en san simón ticumac cdmx (¡Están contratando!)

AI Native Data Engineer
PALO IT —Ciudad de México, CDMX
- Tiempo completo
- Trabajo híbrido
- Azure
- Administración de personal
- ERP systems
Postulación rápida
Software Development Advisor
NTT Ltd —Ciudad de México, CDMX
- Tiempo completo
- Azure
- Administración de personal
- Informática
Sistemas Distribuidos
Kyndryl —Ciudad de México, CDMX
- Tiempo completo
- Informática
- Inglés
- Licenciatura

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AI Native Data Engineer

PALO IT -
Ciudad de México, CDMX

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Información del empleo

Tiempo completo

Cualificaciones

Looker
Power BI
Azure
Administración de personal
Riesgos de préstamos
Spark
ERP systems
Inglés
Google Cloud Platform
Tableau
SQL
Análisis de riesgo
Apache
Kafka
Redshift
Python

Descripción completa del empleo

Who We Are

Building the AI-first frontier enterprise.

We are a global technology consultancy with a trademarked, AI-first approach—Gen-e2. It redefines how enterprises build digital products and transform their organizations with AI. We do the right thing, and we do it right. We're proud to be a World Economic Forum New Champion, and a B Corp-certified company.

We are small enough to care locally, big enough to deliver globally (10 countries, 450+ experts from 50+ nationalities)
We are becoming an agentic organization, adopting the AI-native operating model we bring to our clients.
We are robust and resilient (100% independent, 0 debt, founded 2009)
We are AI-native professionals who invest in what we believe and work as a collective intelligence
We are positive, courageous and deliver at the leading edge.

Your Role

As a Data Engineer for a leading insurance company, you will play a key role in building a data-driven decision-making culture. You will design resilient and secure solutions that transform raw data into strategic insights for underwriting, risk management, fraud prevention, and customer experience.

Design, build, and maintain scalable and secure data pipelines in cloud environments (AWS, GCP, or Azure).
Integrate large volumes of structured and unstructured data from core insurance systems (such as policies, claims, CRM, ERP).
Automate data ingestion, transformation, and quality processes using tools such as Apache Spark, dbt, Kafka, and Airflow.
Build and maintain modern data lakes and warehouses (Snowflake, BigQuery, Redshift).
Implement data quality, lineage, and governance validations (Great Expectations, Deequ, OpenMetadata).
Ensure compliance with data regulations (GDPR, SOC2, etc.) and internal security policies.
Design optimized datasets to enable machine learning models, predictive analytics, and dashboards (Power BI, Looker, Tableau).
Collaborate with data scientists, architects, and business stakeholders to democratize access to trusted data.
Document data architectures, pipelines, transformation standards, and lineage.

Who You Are.

Experience with modern data processing frameworks: Spark, databricks, dbt, Kafka, Apache Beam.
Strong command of Python and advanced SQL.
Hands-on experience with Azure cloud platforms (Data Factory, Synapse).
Knowledge of modern Data Lake / Data Warehouse design (Snowflake, Redshift, BigQuery).
Familiarity with DataOps, testing, and CI/CD practices in data pipelines.
Experience with workflow orchestration systems such as Airflow or Dagster.

Understanding of data quality and governance frameworks: Great Expectations, Deequ, DataHub, OpenLineage.
Knowledge of streaming technologies: Kafka, Pub/Sub, Kinesis.
Upper-intermediate English level (B2 or higher) for global collaboration.

Nice to Have

Familiarity with event-driven architectures and microservices.
Previous experience in the financial or insurance sector.
Certifications such as Azure Data Engineer or Fabric Data Engineer.
Experience with MLOps solutions (Vertex AI, SageMaker, MLFlow).

AI-Native Engineering (Core Expectation)

Use Generative AI coding tools (e.g., GitHub Copilot, Cursor) as a first-class engineering assistant for:
Code scaffolding and refactoring
Code generation and optimisation
Test-cases and documentation generation
Build applications through AI-driven development practices, including:
AI-assisted debugging and troubleshooting
Intelligent code completion and pattern recognition
Automated documentation generation
Apply prompt engineering best practices for reliable, repeatable engineering outcomes.
Validate GenAI output (determinism checks, guardrails, fallback logic).

More About PALO IT

Our clients include some of the world’s most successful companies. We collaborate with leading enterprises, next-generation businesses and frontier partners, shaping what comes next, helping them scale AI and solve complex business and technology challenges.

What We Offer

Stimulating working environments
Unique career path
International mobility
Internal R&D projects (including Gen-e2)
Knowledge sharing
Personalized training via PALO IT Academy
Entrepreneurship & intrapreneurship

For more on our team culture and benefits, check out our careers page.

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machine learning platform engineer empleos en san simón ticumac cdmx

AI Native Data Engineer

Software Development Advisor

Sistemas Distribuidos

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More About PALO IT

Our clients include some of the world’s most successful companies. We collaborate with leading enterprises, next-generation businesses and frontier partners, shaping what comes next, helping them scale AI and solve complex business and technology challenges.

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machine learning platform engineer empleos en san simón ticumac cdmx

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Your Role

More About PALO IT Our clients include some of the world’s most successful companies. We collaborate with leading enterprises, next-generation businesses and frontier partners, shaping what comes next, helping them scale AI and solve complex business and technology challenges.

More About PALO IT

Our clients include some of the world’s most successful companies. We collaborate with leading enterprises, next-generation businesses and frontier partners, shaping what comes next, helping them scale AI and solve complex business and technology challenges.