Senior Data Engineer: Production ML Pipelines (Python/AWS)

Jobgether SRL; Colombia; On-site; COP 90,000,000 - 135,000,000; Full time

Benefits offered by this job

Ongoing training

Career growth

Flexible working arrangements

Business travel possible

Job summary

Lever, Inc. in Colombia is seeking a Senior Data Engineer (Python/AWS/ML Pipelines) to build and operate large-scale data and machine learning pipelines in a production environment.

You will work across data engineering, cloud infrastructure, and ML operations to turn models into reliable production workflows, focusing on Python, ETL, Airflow, and scalable ML infrastructure in a distributed Agile team.

Qualifications

• Strong experience with Python for data engineering and production systems.
• Proven track record building production data or ML pipelines.
• Hands-on experience with Apache Airflow for workflow orchestration.
• Experience with AWS Glue, Step Functions, and SageMaker.
• Experience deploying ML models on AWS.
• Experience with AWS cloud services at scale.

Responsibilities

• Build, maintain, and improve production-grade data and ML pipelines for forecasting.
• Develop ETL and data-processing workflows using Python.
• Design and orchestrate workflows using Airflow and AWS Step Functions.
• Use AWS Glue for data processing and pipeline execution.
• Deploy and operationalize ML models using SageMaker.
• Collaborate with Data Scientists and ML Engineers to productionize solutions.
• Monitor pipeline health and troubleshoot production issues.
• Contribute to architectural decisions for the data platform.

Skills

Strong English communication

Problem-solving

Independent and collaborative

Tools

Python

Apache Airflow

AWS Step Functions

AWS Glue

AWS SageMaker

AWS Cloud

GCP

Job description

Lever, Inc. in Colombia is seeking a Senior Data Engineer (Python/AWS/ML Pipelines) to build and operate large-scale data and machine learning pipelines in a production environment.

You will work across data engineering, cloud infrastructure, and ML operations to turn models into reliable production workflows, focusing on Python, ETL, Airflow, and scalable ML infrastructure in a distributed Agile team.