AI Data Engineer

Company Description Taurus is a growing technology organization focused on building data-driven solutions that support advanced analytics and AI-powered decision-making. The team collaborates across disciplines to design and maintain robust data platforms that enable innovative products and services. Taurus values technical excellence, integrity, and practical problem-solving, providing team members with opportunities to shape modern data architectures. The company encourages continuous learning, experimentation, and the use of emerging tools and frameworks. Team members are supported in developing their skills while contributing to impactful, scalable data solutions.

Role Description The AI Data Engineer is a full-time, on-site role based in the Albany, New York Metropolitan Area. This role is responsible for designing, building, and maintaining data pipelines and infrastructure that power AI and analytics solutions. Day-to-day tasks include developing and optimizing ETL processes, modeling data structures to support machine learning and reporting needs, and managing data warehousing environments. The AI Data Engineer collaborates with data scientists, analysts, and software engineers to ensure data is reliable, well-structured, and accessible for various use cases. The role also involves monitoring data quality, improving performance, documenting technical solutions, and contributing to best practices for data governance and security.

Qualifications

• Strong data engineering skills, including experience with data pipelines, data integration, and scalable data infrastructure.
• Proficiency in data modeling and designing schemas for analytical and AI workloads.
• Hands-on experience with Extract Transform Load (ETL) processes and related tools or frameworks.
• Knowledge of data warehousing concepts and platforms, and familiarity with modern cloud or on-premise data warehouse solutions.
• Solid data analytics skills, including the ability to interpret data, create meaningful datasets, and support reporting and AI use cases.
• Proficiency in programming languages commonly used in data engineering (such as Python, SQL, or Scala) and experience with version control systems.
• Understanding of AI and machine learning workflows and how data engineering supports model development and deployment.
• Experience with relational and NoSQL databases, and familiarity with data governance, security, and compliance practices.
• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related field, or equivalent practical experience.
• Ability to work collaboratively on-site, communicate clearly with technical and non-technical stakeholders, and manage multiple priorities effectively.