Data Engineer - Data Foundry Engineer

The Data Science team at TRACTIAN focuses on extracting valuable insights from vast amounts of industrial data. Using advanced statistical methods, algorithms, and data visualization techniques, this team transforms raw data into actionable intelligence that drives decision-making across engineering, product development, and operational strategies. The team constantly works on optimizing prediction models, identifying trends, and providing data-driven solutions that directly enhance the company’s operational efficiency and the quality of its products.

We're looking for a Data Engineer with a strong engineering foundation and comfort with AI workflows to join our Data Foundry team. In this role, you'll be the bridge between our model training and data annotation teams, building the pipelines and infrastructure that turn raw, messy data into gold-standard datasets ready for AI consumption.

→Comfortable jumping into ongoing codebases and optimizing work built by others, without needing to start from scratch

→Technology-agnostic: you evaluate tools based on what the project needs, adopt new ones quickly, and don't get attached to a specific stack

→At ease in fast-moving environments where priorities shift and the right answer isn't always obvious

→Engineering-first mindset: you think in pipelines, own outcomes, and care about the quality of what you ship

→Driven by curiosity and innovation, not by comfort with a known toolset

→Experience making architectural decisions and contributing to the technical growth of a team, formally or informally

→Go, for high-performance pipeline components

→dbt for transformation layer modeling

→Open table formats: Delta Lake, Apache Iceberg, or Hudi

→Data quality frameworks such as Great Expectations or Soda

→Cloud experience, preferably OCI (our current migration target). AWS, GCP, or Azure background is also valued

→Rapid prototyping with Streamlit or similar tools. The use of LLMs and GenAI to speed up internal tooling and experimentation is actively encouraged

→Experience with data annotation workflows or training dataset pipelines