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Data Engineer Airflow Jobs in Hawaii (NOW HIRING)

Vultus Inc

Machine Learning Engineer

Honolulu, HI · On-site +1

$110K - $145K/yr

You will work closely with data scientists, software engineers, and product teams to build ... Airflow * Kafka * Azure ML * AWS SageMaker * Google Vertex AI * FastAPI * Flask Preferred ...

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Data Engineer Airflow information

What is the difference between Data Engineer Airflow vs Data Engineer?

AspectData Engineer AirflowData Engineer
Primary FocusWorkflow orchestration and pipeline automation using AirflowData collection, storage, transformation, and pipeline development
Required SkillsPython, Airflow, ETL processes, cloud platformsSQL, Python, ETL, data modeling, cloud services
Work EnvironmentData teams, cloud environments, automation pipelinesData warehouses, big data platforms, cloud infrastructure
CertificationsAirflow certifications, Python, cloud certificationsSQL, cloud certifications, data engineering certifications

While both roles involve data pipeline work, Data Engineer Airflow specializes in designing and managing workflows with Airflow, focusing on automation and orchestration. In contrast, Data Engineer has a broader scope, including data storage, transformation, and pipeline development across various tools and platforms.

What does a data engineer specializing in Airflow do?

A Data Engineer specializing in Airflow is responsible for designing, building, and maintaining data pipelines using Apache Airflow, an open-source workflow orchestration tool. Their main job is to automate, schedule, and monitor complex data workflows, ensuring data moves reliably between systems and is processed efficiently. They often collaborate with data scientists, analysts, and other engineers to make sure that data is accessible, accurate, and up to date for business needs. Expertise in Airflow helps streamline data operations, optimize performance, and improve data pipeline reliability.

What are the key skills and qualifications needed to thrive as a data engineer specializing in Airflow, and why are they important?

To thrive as a Data Engineer with an Airflow focus, you need strong programming skills in Python, expertise in data pipeline design, and experience with distributed systems, often supported by a degree in computer science or a related field. Familiarity with Apache Airflow, cloud platforms (like AWS or GCP), and database technologies, as well as certifications in cloud data engineering, are typically required. Outstanding problem-solving, attention to detail, and effective communication help you collaborate on complex data workflows and troubleshoot issues efficiently. These skills ensure robust, scalable, and reliable data infrastructure, enabling organizations to make data-driven decisions with confidence.

How does a data engineer specializing in Airflow typically collaborate with data scientists and analysts?

Data Engineers working with Airflow play a crucial role in enabling data scientists and analysts to access reliable, up-to-date data. They design and maintain ETL pipelines that automate data movement and transformation, ensuring data is clean and available for analysis. Collaboration often involves gathering requirements, troubleshooting pipeline issues, and optimizing data workflows to meet the needs of downstream users. Effective communication and documentation are essential, as data engineers must align technical solutions with the analytical goals of the broader team.

What are popular job titles related to Data Engineer Airflow jobs in Hawaii?

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What job categories do people searching Data Engineer Airflow jobs in Hawaii look for?

The top searched job categories for Data Engineer Airflow jobs in Hawaii are:

What cities in Hawaii are hiring for Data Engineer Airflow jobs?

Cities in Hawaii with the most Data Engineer Airflow job openings:

Data Engineer Airflow jobs near you
Infographic showing various Data Engineer Airflow job openings in Hawaii as of June 2026, with employment types broken down into 3% Internship, 6% As Needed, 42% Full Time, 20% Part Time, 26% Contract, and 3% Nights. Highlights an 75% Physical, 4% Hybrid, and 21% Remote job distribution.

Machine Learning Engineer

Vultus Inc

Honolulu, HI • On-site, Remote

$110K - $145K/yr

Full-time

Posted 23 days ago

Job description

Machine Learning EngineerJob Summary

We are looking for a talented Machine Learning Engineer to design, develop, and deploy scalable machine learning models that solve complex business problems. The ideal candidate should have experience in data preprocessing, model development, feature engineering, and deploying ML solutions in production environments. You will work closely with data scientists, software engineers, and product teams to build intelligent applications.

Key Responsibilities
  • Design, build, and deploy machine learning models for predictive analytics and automation.
  • Collect, clean, and preprocess structured and unstructured datasets.
  • Perform feature engineering and model optimization to improve performance.
  • Train, validate, and evaluate machine learning models using industry best practices.
  • Deploy ML models using cloud platforms and containerization technologies.
  • Monitor model performance and retrain models as needed.
  • Collaborate with cross-functional teams to understand business requirements.
  • Develop APIs and services for model inference.
  • Document model architecture, experiments, and deployment processes.
  • Stay updated with the latest advancements in AI and machine learning technologies.
Required Qualifications
  • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
  • 3–6 years of experience in Machine Learning or Artificial Intelligence.
  • Strong programming skills in Python.
  • Experience with supervised and unsupervised learning algorithms.
  • Hands-on experience with TensorFlow, PyTorch, or Scikit-learn.
  • Knowledge of statistics, probability, and linear algebra.
  • Experience with SQL and NoSQL databases.
  • Familiarity with REST APIs and microservices architecture.
  • Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
  • Understanding of CI/CD pipelines for ML deployment.
Primary Skills
  • Python
  • Machine Learning
  • Deep Learning
  • TensorFlow
  • PyTorch
  • Scikit-learn
  • Pandas
  • NumPy
  • Feature Engineering
  • Model Deployment
  • Data Preprocessing
  • SQL
  • Docker
  • Kubernetes
  • Git
Secondary Skills
  • NLP (Natural Language Processing)
  • Computer Vision
  • MLOps
  • Apache Spark
  • MLflow
  • Airflow
  • Kafka
  • Azure ML
  • AWS SageMaker
  • Google Vertex AI
  • FastAPI
  • Flask
Preferred Qualifications
  • Experience with large-scale ML model deployment.
  • Knowledge of Generative AI and Large Language Models (LLMs).
  • Experience with vector databases such as Pinecone, Milvus, or FAISS.
  • Familiarity with prompt engineering and Retrieval-Augmented Generation (RAG).
  • Experience with Agile/Scrum methodologies.
Experience

3–6 Years

Employment Type

Full-Time

Work Location

Remote / Hybrid / On-site

Salary Range

$110,000 – $145,000 per year (Based on experience and location)