Senior Data Engineer - Cloud Ingest & DataOps Lead (Remote)

Aesa (A-Evangelista Sa); Rosario; Remoto; ARS 139.086.000 - 216.357.000; Tempo pieno

Vantaggi offerti da questo lavoro

Professional growth

Competitive compensation

Flexibility

Remote work

Descrizione del lavoro

Endava is seeking a Senior Data Engineer focused on data ingestion and platform integrations for LATAM. You will build and operate production-grade collectors, connectors, and pipelines across AWS, Snowflake, and DBT-based transformations, while working closely with customers to understand environments and requirements.

You will design scalable ingestion architectures, collaborate with cross-functional teams, and contribute to data quality, observability, and incident response.

Competenze

• 5+ years of professional experience in data engineering or a related software engineering role.
• Strong hands-on experience with Snowflake and DBT in production.
• Proficient in Python or Go and SQL for data pipelines.

Mansioni

• Design, develop, and maintain production-grade data collectors, ingestion services, and pipelines.
• Integrate enterprise data sources including identity providers, cloud platforms, networking, and security systems.
• Collaborate with customers and internal teams to understand environments and integration constraints.
• Develop and maintain scalable ingestion architectures for high-volume data sources.
• Lead or support incident resolution and provide preventive improvements.

Conoscenze

Python

Go

REST APIs

SQL

NoSQL

Docker

Kubernetes

Airflow

dbt

Snowflake

AWS

CI/CD

Terraform

Observability

Customer-facing

Strumenti

Docker

Kubernetes

Descrizione del lavoro

Endava is seeking a Senior Data Engineer focused on data ingestion and platform integrations for LATAM. You will build and operate production-grade collectors, connectors, and pipelines across AWS, Snowflake, and DBT-based transformations, while working closely with customers to understand environments and requirements.

You will design scalable ingestion architectures, collaborate with cross-functional teams, and contribute to data quality, observability, and incident response.