Technical Operations Engineer, Semi Senior

Darwoft

contract

Posted on: 9/24/2025

Origin: • 🇦🇷 Argentina

✨ AI Apply

Senior

Amazon RedshiftAWSBigQueryCloudDistributed SystemsElasticSearchETLGoogle Cloud PlatformPythonPyTorchScikit-LearnSQLTensorflow

About the role

Own and ensure the health, stability, and performance of AI-driven data platforms, pipelines, and infrastructure
Monitor, troubleshoot, and optimize complex ETL/ELT workflows, ensuring data quality and availability
Develop Python-based scripts and tools to automate deployments, workflows, and system maintenance
Collaborate with Engineering, Product, and Customer Success to resolve complex operational issues and ensure seamless data delivery
Build and maintain detailed operational runbooks, incident playbooks, and system guides
Work in an Agile environment with engineers, product managers, and data scientists to operationalize analytics for Life Sciences & Healthcare datasets
Operationalize data architectures, optimize pipelines, and deliver robust solutions that drive internal efficiency and customer satisfaction

Bachelors or Masters in Computer Science, Engineering, or related field
4+ years in Technical Operations, DevOps, or SRE with focus on data platforms
Proven experience managing enterprise-grade data services (Data Pipelines, Data Lakes, Warehouses)
Expert Python skills for automation and operational tooling
Strong cloud experience (AWS or GCP) including compute, storage, databases, containerization, orchestration
SQL proficiency with BigQuery, Redshift, or Snowflake
Deep knowledge of ETL/ELT best practices, data governance, and compliance
Ability to diagnose complex distributed systems issues with strong RCA skills
Excellent communication (verbal & written) and experience creating technical documentation
Collaborative, proactive mindset with strong ownership
Ability to work with stakeholders in EST timezone
Experience in regulated industries (healthcare, finance) and compliance (HIPAA)
Nice to have: Experience in Life Sciences / Healthcare data domain; Knowledge of MLOps and deploying AI/ML models; Familiarity with data visualization tools (Looker, PowerBI); Experience with Elasticsearch or search technologies; Understanding of ML frameworks (TensorFlow, PyTorch, Scikit-learn, MLFlow)