Data/ML Engineer

About SailPlan

SailPlan is a cutting-edge technology company that is dedicated to transforming the future of maritime transportation. SailPlan offers a range of innovative solutions and services that enable its clients to optimize their operations and reduce their environmental impact.

SailPlan works with some of the most important names in the shipping industry to deliver a cleaner future for the world. SailPlan’s team comprises of experts with a diverse range of skills and experience, including naval architects, data scientists, and software engineers. The company’s collaborative and dynamic work culture fosters innovation and creativity, allowing the team to develop cutting-edge solutions that drive the industry forward. By combining state-of-the-art technology and a commitment to sustainability, SailPlan is leading the way towards a greener and more efficient maritime industry.

At SailPlan, you will be part of a fast-growing team, will wear many hats and have ownership over building key aspects of our platform. You will work within a collaborative environment to build the next generation of technology for the maritime industry.

If you think you have the right stuff, we are looking for YOU.

Location

Role Description and Responsibilities

We are seeking an experienced Data Engineer to focus on productionalizing machine learning models within Google Cloud Platform (GCP) while collaborating on ETL design and planning. This role will be responsible for architecting, implementing, and maintaining the infrastructure needed for model training, retraining, and deployment, ensuring high-quality ML performance monitoring and external output serving. 

The ideal candidate has expertise in ML model operationalization, cloud architecture, and data pipeline orchestration, working closely with data scientists, cloud engineers, and analysts to bridge the gap between data engineering and MLOps, while also ensuring that model outputs are seamlessly integrated into analytics and decision-making systems.

Core Requirements and Qualifications

• ML Model Productionalization:

• • • Select and implement the appropriate GCP services for scalable ML workflows, including model training, retraining, and deployment
    • Develop automated monitoring and performance tracking for deployed models, surfacing quality metrics internally and ensuring external services receive high-quality outputs
    • Optimize model deployment pipelines to ensure efficient versioning, retraining triggers, and drift detection

• Collaboration on ETL Development:

• • • Work alongside data engineers to design and optimize data pipelines that support machine learning models
    • Ensure seamless integration between data ingestion, transformations, and ML pipelines, leveraging BigQuery and DBT
    • Coordinate with sensor and instrumentation engineers to facilitate the ingestion of real-time sensor data for predictive modeling

• MLOps & Cloud Infrastructure:

• • • Architect and implement CI/CD pipelines for ML models, enabling automated deployment, testing, and rollback strategies
    • Design cloud infrastructure that supports scalable and cost-efficient ML model training in production environments
    • Implement logging, alerting, and monitoring to proactively identify issues with models and data pipelines

• Model Output Integration & Analytics:

• • Ensure ML model outputs are easily accessible and consumable by analytics, dashboards, and external services
  • Work closely with data analysts and cloud engineers to optimize Looker integrations and visualization pipelines for ML-driven insights
  • Maintain and document model lifecycle processes, ensuring clarity and reproducibility across the team

Required Skills:

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field
• Strong experience in MLOps and machine learning model operationalization, particularly within GCP
• Proficiency in SQL and Python, with experience in data manipulation, feature engineering, and ML model deployment
• Hands-on experience with CI/CD pipelines, version control (Git), and infrastructure-as-code (Terraform, Cloud Build)
• Experience working with data pipelines (specifically time-series data) and collaborating with data engineers to support ML workflows
• Excellent problem-solving skills and a proactive, collaborative mindset

Preferred Qualifications

• Familiarity with the maritime/shipping domain, including knowledge of sensor data and operational challenges
• Experience with dbt and BigQuery for efficient data transformation
• Knowledge of LookML and Looker dashboards, especially for surfacing ML insights
• Experience working with real-time streaming and high-fidelity time series data
• Understanding of data governance, security, and compliance best practices in cloud-based environments

Other information or Special Requirements

This is a full time position. US citizen or work permit required.