Senior Data Ingestion Engineer – Remote LATAM

Boehringer Ingelheim Gmbh; Neuquén; Remote; ARS 139,086,000 - 216,357,000; Full time

Job summary

Darwoft is seeking a Senior Data Engineer focused on data ingestion and integrations to serve enterprise customers remotely in LATAM. You will design and operate connectors, ingestion pipelines, and data collectors while collaborating with customers and engineering teams to ensure secure, scalable solutions.

Responsibilities include building data ingestion services, integrating diverse data sources, and deploying pipelines using Python/Go, Docker/Kubernetes, and cloud platforms across AWS,

Qualifications

• 10+ years of professional Software/Data Engineering
• Proficient in Python and Go
• Experience building data ingestion pipelines
• Cloud experience (AWS/Azure/GCP)
• Experience with Docker and Kubernetes
• Strong REST API design/consumption
• SQL and NoSQL databases
• Ability to work with customers and translate requirements

Responsibilities

• Design, develop, and maintain data collectors, connectors, and ingestion services.
• Integrate enterprise data sources including IdPs, cloud platforms, SASE, networks, endpoints, apps, and SIEMs.
• Work with customers to understand environments, data flows, APIs, and integration constraints.
• Design scalable ingestion architectures for high-volume data such as events and logs.
• Build integrations using REST APIs, cloud-native services, event-driven patterns.

Skills

Python

Go

REST APIs

Data ingestion

Cloud infrastructure

Distributed systems

Data modeling

Debugging

Tools

Docker

Kubernetes

AWS

Azure

GCP

SQL

NoSQL

Git

CI/CD

Job description

Darwoft is seeking a Senior Data Engineer focused on data ingestion and integrations to serve enterprise customers remotely in LATAM. You will design and operate connectors, ingestion pipelines, and data collectors while collaborating with customers and engineering teams to ensure secure, scalable solutions.

Responsibilities include building data ingestion services, integrating diverse data sources, and deploying pipelines using Python/Go, Docker/Kubernetes, and cloud platforms across AWS,