Data Engineer Senior

Leadout Capital; Ciudad de México; On-site; MXN 900,000 - 1,200,000; Full time

Job summary

Leadout Capital is seeking a Data Engineer in Mexico City to design, build, and optimize scalable data pipelines and models powering analytics and insights. You will work across modern platforms such as dbt, Snowflake, Informatica, and Airflow to ensure reliable data for decision-making, in a fast-paced, data-driven environment.

The role requires 5–7 years of hands-on data engineering experience, strong SQL, and scripting skills (Python or similar).

Qualifications

• 5–7 years of hands-on data engineering experience.
• Strong SQL and scripting skills (Python or similar).
• Experience with dbt, Snowflake, Informatica, and Airflow.
• Solid understanding of data modeling (dimensional, star/snowflake).

Responsibilities

• Design, build, and maintain ETL/ELT pipelines across tools (dbt, Snowflake, Informatica, Airflow).
• Develop and optimize data models for analytics, reporting, and ML use cases.
• Integrate data from multiple systems to create unified datasets.
• Ensure data accuracy, completeness, and quality through validation and monitoring.
• Collaborate with analysts, product managers, and business teams to translate requirements into data solutions.
• Support data governance and quality initiatives and adhere to standards.
• Contribute to improving data engineering processes, tooling, and documentation.
• Troubleshoot and resolve data pipeline issues to optimize performance and scalability.
• Stay updated with emerging data technologies and best practices.

Skills

SQL

Python

Cross-functional collaboration

Attention to detail

Problem-solving

Tools

dbt

Snowflake

Informatica

Apache Airflow

Git

Job description

Data Engineer

DET TEAM
Mexico City

We are seeking a Data Engineer with 5–7 years of experience to design, build, and optimize scalable data pipelines and models that power analytics and business insights. You’ll work across modern data platforms and tools—such as dbt, Snowflake, Informatica, and Airflow—to ensure reliable, high-quality data for decision‑making. This role is ideal for an engineer who enjoys problem‑solving, collaboration, and continuous learning in a fast‑paced, data‑driven environment.

Key Responsibilities

• Design, build, and maintain automated ETL/ELT pipelines using tools like dbt, Snowflake, Informatica, and Airflow.
• Develop and optimize data models to support analytics, reporting, and machine learning use cases.
• Integrate data from multiple systems to create unified, high‑quality datasets.
• Ensure data accuracy, completeness, and consistency through validation and monitoring.
• Collaborate with data analysts, product managers, and business teams to translate requirements into efficient data solutions.
• Support data governance and quality initiatives, ensuring compliance with standards and best practices.
• Contribute to the continuous improvement of data engineering processes, tools, and documentation.
• Troubleshoot and resolve issues in data pipelines, optimizing for performance and scalability.
• Stay current with emerging data technologies and engineering best practices.

Desired Skills & Experience

• 5–7 years of hands‑on experience in data engineering or data platform development.
• Strong proficiency in SQL and working knowledge of Python or another scripting language.
• Experience with dbt for data transformation and Snowflake for cloud data warehousing.
• Practical understanding of Informatica and Apache Airflow for data integration and orchestration.
• Solid grasp of data modeling techniques (dimensional, star/snowflake schemas).
• Familiarity with CI/CD workflows, Git, and modern DevOps practices.
• Knowledge of data quality frameworks and monitoring tools.
• Strong problem‑solving skills and attention to detail in ensuring data reliability.
• Effective communicator and team player, able to work in cross‑functional environments.
• Passion for data‑driven decision‑making and improving data infrastructure.