Design, build, and maintain scalable data pipelines and architectures to support analytical and operational workloads.
Develop and optimize ETL/ELT pipelines, ensuring efficient data extraction, transformation, and loading from various sources.
Work closely with backend and platform engineers to integrate data pipelines into cloud-native applications.
Manage and optimize cloud data warehouses, primarily BigQuery, ensuring performance, scalability, and cost efficiency.
Implement data governance, security, and privacy best practices, ensuring compliance with company policies and regulations.
Collaborate with analytics teams to define data models and enable self-service reporting and BI capabilities.
Develop and maintain data documentation, including data dictionaries, lineage tracking, and metadata management.
Monitor, troubleshoot, and optimize data pipelines, ensuring high availability and reliability.
Stay up to date with emerging data engineering technologies and best practices, continuously improving our data infrastructure.
Certified SnowPro.
Experience collaborating with distributed teams across North America and Latin America.
5+ years of experience in data engineering, building modern cloud-based data platforms.
Strong hands-on experience with Snowflake, including performance tuning, security, and data modeling.
Advanced SQL skills for analytics engineering and data transformation.
Proficiency in Python for data processing, automation, and orchestration.
Experience with dbt or similar analytics engineering frameworks.
Familiarity with orchestration tools such as Dagster or Apache Airflow.
Experience enabling or supporting machine learning, feature engineering, or AI-driven use cases on top of data platforms.
Solid understanding of data privacy, governance, and compliance best practices.
Strong problem-solving skills and a pragmatic, delivery-oriented mindset.
Strong problem-solving skills, with the ability to debug and optimize complex data workflows.
Excellent English communication and collaboration skills.
Experience with Snowflake Cortex, Snowpark, or ML workflows integrated with Snowflake.
Familiarity with GenAI or LLM-enabled analytics use cases (e.g., semantic layers, AI-assisted insights, decisioning).
Experience with Databricks or hybrid Snowflake + Databricks architectures.
Exposure to real-time or near-real-time data pipelines.
Experience with BI tools (e.g., Looker, Tableau, Power BI).
Infrastructure as Code experience (e.g., Terraform) in data environments.
Familiarity with CDPs, marketing platforms, and digital experience ecosystems (e.g., Segment, Contentful, personalization tools).