Austin, TX

Handraise is seeking a skilled Data Engineer to join our Machine Learning team. In this high-impact role, you will design, build, and optimize data pipelines and infrastructure to support AI-powered machine learning models for our reputation management platform. You will collaborate closely with data scientists, software engineers, and ML engineers to create efficient, scalable data systems across our company's ecosystem.

About Handraise

Founded in 2023, Handraise exists to unlock the power of news for the world’s top brands and beyond. We’re looking for craftspeople who are eager to collaborate with the top Communications professionals in the world to revolutionize how they understand and drive business impact from their news and social media. We're passionate about building a company that solves real problems for Communications professionals, journalists, and eventually broader news audiences. We're all about enabling our team through autonomy and ownership, and we champion a variety of viewpoints. We work hard, have a competitive spunk, and believe in living life beyond work so we can show up the healthiest, happiest versions of ourselves and do our best work for ourselves and our team. We're a tight group of collaborators in Austin, TX, backed by some of the best investors in the world, including Floodgate, Silverton Partners, and Bill Wood Ventures. Our Founder previously was the Founder of PR analytics startup, TrendKite, which was sold to Cision (NYSE:CISN) in 2019 for $225M.

Responsibilities

• Data Pipeline Development: Design, build, and maintain data pipelines that support machine learning workflows, including data ingestion, transformation, and validation.
• Data Infrastructure Management: Set up and manage scalable cloud-based data platforms optimized for machine learning workloads.
• Feature Engineering: Collaborate with data scientists and ML engineers to understand requirements and develop efficient data pipelines to produce high-quality, reliable features for ML models.
• Data Storage and Processing: Work with structured and unstructured data sources, implementing best practices in data storage, management, and retrieval.
• ETL Automation: Develop automated ETL workflows, ensuring data integrity, accuracy, and availability for downstream machine learning tasks.
• Optimization and Monitoring: Collaborate with MLOps to implement monitoring, logging, and alerting systems for data pipelines and machine learning models in production, optimizing performance and minimizing latency.
• MLOps Implementation: Collaborate with MLOps, data scientists, and machine learning engineers to operationalize machine learning models. Create and manage data pipelines for training and deploying machine learning models.
• Collaboration: Partner with data science and engineering teams to improve data quality, promote reusability, and enhance machine learning model performance.
• Data Security and Compliance: Ensure all data practices adhere to industry best practices for security, compliance, and privacy.
• Stay Current: Evaluate and integrate new tools and technologies to enhance the data ecosystem.

Requirements

• Experience: 2+ years in data engineering or a similar role, with a strong focus on supporting machine learning initiatives.
• Machine Learning Fundamentals: Proficiency in the core concepts of machine learning, including familiarity with supervised and unsupervised learning methods, model evaluation metrics, feature engineering, and data preprocessing techniques. Strong understanding of how to support data pipelines that enable scalable machine learning workflows, with experience in training, testing, and deploying models in production. Experience with tools and frameworks such as TensorFlow, PyTorch, Scikit-Learn, or similar is a plus.
• ML Pipeline Automation: Experience with tools like Kubeflow, MLflow, TFX, Ray, Dagster, or Airflow for automating ML workflows.