Ingeniero Líder de Datos

• 8+ years of professional experience in data engineering, with deep expertise in both batch and streaming data architectures.
• Proven experience building scalable ETL/ELT pipelines and distributed data processing frameworks.
• Strong proficiency in Python and modern ETL tools (GCP Cloud Dataflow, Fivetran, dbt, Apache Spark).
• Hands-on experience with cloud-based data platforms such as BigQuery, Snowflake, Redshift, or Databricks.
• Solid understanding of Customer Data Platforms (CDPs) like Twilio Segment or RudderStack.
• Experience with streaming technologies (GCP Pub/Sub, Kafka, AWS SNS) is highly desirable.
• Background in multi-tenant SaaS architectures is a strong plus.
• GCP.

Interfell connects LATAM-based IT talent with leading companies, driving the future of work through remote collaboration. The Lead Data Engineer will lead the development of the company’s data ecosystem, designing and implementing a data platform. This role involves close collaboration with product and technical teams to enable data-driven insights, real-time personalization, predictive modeling, and targeted marketing initiatives, while defining the architecture, tools, and strategy for data pipelines, data lakes, cloud data warehousing, and integrations. .

• Lead the design and implementation of a scalable Customer Data Platform (CDP) integrating batch and real-time data from multiple sources (DMS, CRMs, social networks, web traffic).
• Design and develop data pipelines using tools like Fivetran, dbt, Apache Spark, and Airflow to ensure high-performance, reliable data flow.
• Architect and manage data lakes and cloud data warehouses (e.g., BigQuery, Redshift, Snowflake, Databricks).
• Build and optimize streaming pipelines with GCP Pub/Sub, Kafka, or AWS SNS to support real-time analytics and personalization.
• Develop integrations with Customer Data Platforms (CDPs) such as Twilio Segment or RudderStack and connect with marketing automation systems.
• Collaborate with Product, Engineering, and Data Science teams to deliver data-driven insights powering personalization, predictive modeling, and segmentation.
• Ensure strong alignment between data initiatives and the product roadmap, prioritizing data products that deliver measurable customer impact.
• Define architecture and strategy for multi-tenant SaaS data solutions, ensuring scalability, reliability, and data security.
• Drive adoption of modern technologies and best practices in data engineering, promoting a culture of technical excellence.
• Maintain high standards for data quality, governance, and compliance with industry regulations.