Senior Data Engineer, Python, AWS, ML Pipelines

Job not on LinkedIn

🕒 April 1

🌐 Portugal, Brazil – Remote

🇵🇹 Portugal – Remote 🇧🇷 Brazil – Remote

⏰ Full Time

🟠 Senior

🔙 Backend Engineer

👻 Ghost score 47%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Vigil

WebsiteLinkedInAll Job Openings

51 - 200 employees

💼 Consulting

🏥 Healthcare

📣 Marketing

Consulting • Healthcare • Marketing

Vigil is a tech consultancy company specializing in software engineering and cloud transformation. They focus on creating exceptional digital products that drive business value and are committed to innovation. Vigil values recognition, appreciation, work/life balance, and opportunities for growth, ensuring happiness and motivation for their teams. They invite clients to collaborate with them to bring projects to life.

📋 Description

• Building and maintaining data and machine learning pipelines in production • Developing ETL and data processing workflows using Python • Orchestrating workflows using tools such as Airflow and AWS Step Functions • Working with AWS Glue for data processing and pipeline execution • Supporting the deployment of machine learning models into production using SageMaker • Working closely with Data Scientists and ML Engineers to productionise ML models • Monitoring, troubleshooting, and improving pipeline reliability and performance • Contributing to architecture and technical decisions around data platform and ML infrastructure • Working in an agile environment with distributed teams

🎯 Requirements

• Strong experience with Python • Experience building data pipelines and/or ML pipelines in production • Hands-on experience with Airflow • Experience with AWS Step Functions and AWS Glue • Experience deploying ML models using AWS SageMaker • Strong experience with AWS cloud • Experience working with production systems at scale • Experience working in cross-functional teams with Data Scientists • Strong communication skills and ability to work in a distributed team environment. • Strong English communication skills, both written and verbal

🏖️ Benefits

• A culture in which we don’t criticise failure but ensure we learn from our mistakes • An Agile environment where your ideas are welcome • Ongoing Training & Mentoring • The possibility of travel