AI Native Data Engineer
PALO IT Ciudad de México, Mexico
IT Services and IT Consulting · 501-1,000 employees
About the role
You will design and maintain scalable, secure data pipelines to transform raw data into strategic insights for insurance operations. You will also collaborate with stakeholders to democratize data access while ensuring compliance with governance and security standards.
What they look for
Requirements
The role requires strong proficiency in Python, SQL, and modern data processing frameworks like Spark and dbt. Candidates should have hands-on experience with cloud platforms such as Azure and a solid understanding of data lake/warehouse design.
Benefits
Full description
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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