data engineer with ML

  • Seattle, WA
  • Posted 3 days ago | Updated 3 days ago

Overview

Remote
On Site
$60,000 - $80,000
Full Time

Skills

Advanced Analytics
Big Data
Data Engineering
Data Governance
Data Integrity
Data Processing
Databricks
Machine Learning Operations (ML Ops)

Job Details

We are partnering with one of our staffing partners who is searching for a Data engineer with Machine learning experience to design, develop, and optimize scalable data pipelines and infrastructure for ML-driven applications. The ideal candidate will have experience in big data technologies, ETL processes, and model deployment to support advanced analytics and AI initiatives.
Salary $50k-70k slight flexibility for a good candidate.
Due to low rates only direct applicants no c2c candidates please. Candidates will be reached directly by our staffing partner and client
Responsibilities
  • Design and build scalable data pipelines to support machine learning workflows.
  • Develop, optimize, and maintain ETL processes for structured and unstructured data.
  • Work with big data frameworks (Apache Spark, Hadoop, Databricks) to process large datasets.
  • Collaborate with data scientists to integrate ML models into production environments.
  • Implement and manage ML model deployment and monitoring
  • Optimize data storage, retrieval, and performance for machine learning applications.
  • Ensure data integrity, security, and compliance with industry standards.
  • Automate data processing workflows using Python, SQL, or Scala.
Requirements
  • Strong experience in data engineering and machine learning workflows.
  • Hands-on experience with big data technologies (Apache Spark, Hadoop, Snowflake).
  • Proficiency in SQL and Python, with experience in ML libraries (TensorFlow, PyTorch, Scikit-learn).
  • Knowledge of cloud platforms (AWS, Azure, Google Cloud Platform) for ML model deployment.
  • Familiarity with DevOps and MLOps tools, such as:
    • CI/CD Pipelines for ML (MLflow, Kubeflow)
    • Containerization (Docker, Kubernetes)
    • Infrastructure as Code (Terraform, Ansible)
    • Version Control (Git, Bitbucket)
  • Strong understanding of data governance, security, and best practices.
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