Senior Data Engineer

Office in Argentina,

Data DevOps

Facebook LinkedIn Send via email

Looking for something else?

Find a vacancy that works for you. Send us your CV to receive a personalized offer.

Find me a job

Location-specific conditions & benefits*

Choose an option

We are seeking a Senior Data Engineer to lead the design, development, and maintenance of data and ML pipelines on the Domino Data Lab platform. This role focuses on the data engineering and MLOps side of Domino, building reliable data pipelines, managing model lifecycle workflows, and ensuring the platform's data and compute infrastructure runs efficiently and securely. This is not a front-end or application development role.

Responsibilities

Design, build, and maintain robust data pipelines that support analytical and machine learning workloads
Manage the end-to-end lifecycle of data workflows, from ingestion through transformation and delivery
Oversee compute infrastructure to ensure efficient, secure, and reliable platform operations
Troubleshoot and resolve issues affecting pipeline performance and data quality
Collaborate with data scientists and other engineers to support their infrastructure and tooling needs
Establish and promote best practices for platform usage, pipeline architecture, and data workflow design
Automate testing and deployment processes to improve reliability and reduce manual effort
Monitor pipeline health and proactively address bottlenecks or failures
Contribute to the ongoing improvement of internal tools and processes supporting data operations
Document technical designs, workflows, and configurations to support knowledge sharing across the team

Requirements

A minimum of 3 years of relevant experience
Extensive hands-on experience with the Domino Data Lab platform, including Data Sources and Connectors, Datasets, Environments, Projects, Jobs, and Flows, with the ability to architect and troubleshoot end-to-end data pipelines and guide best practices for platform usage
Expert-level proficiency in Python as the primary language for data engineering and pipeline development
Strong command of SQL for data extraction, transformation, and optimization across relational and warehouse systems
Comfortable working with R and Bash across the broader data science toolchain and for automation scripting
Demonstrated experience designing, building, and maintaining ETL/ELT pipelines, including data ingestion, transformation, validation, and orchestration
Hands-on experience with Kubernetes and managed solutions such as EKS, AKS, or GKE, with the ability to deploy, debug, and tune cluster workloads running data and ML jobs
Experience building, optimizing, and troubleshooting container images for data and ML workloads using Docker
Experience building and maintaining CI/CD pipelines using tools such as Jenkins, GitLab CI, GitHub Actions, or Azure DevOps to automate testing and deployment of data pipelines and ML workflows
Working knowledge of at least one major cloud provider, such as AWS, Azure, or GCP, with the ability to reason about data architecture, cost, and security trade-offs
Excellent English proficiency (B2 level or higher)

Nice to have

Experience with Domino Nexus or hybrid/multi-cloud compute orchestration
Experience delivering ML workflows covering training, deployment, monitoring, and retraining, with an understanding of reproducibility and versioning
Practical experience with GenAI, LLMs, or agentic frameworks, including retrieval-augmented generation (RAG), from a data pipeline perspective
Familiarity with orchestration tools such as MLflow, Kubeflow, Airflow, or Domino Flows
Knowledge of model governance, compliance automation, or audit logging frameworks
Experience working in pharma, BFSI, or public sector environments