Data Engineer - Data Foundry Engineer

Data Science at TRACTIAN

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.

What you'll do

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.

Responsibilities

- Design and maintain robust data pipelines to ingest from a wide range of sources, including APIs, documents, websites, and raw sensor data

- Integrate and optimize ETL/ELT processes developed by MLE colleagues, improving performance, reliability, and long-term maintainability

- Own the full dataset lifecycle, from raw ingestion through cleaning, validation, and delivery as training-ready data

- Define and enforce data quality standards and governance practices across the Data Foundry team

- Build and maintain labeling pipeline infrastructure for ML applications, working closely with the annotation team

- Participate in architectural decisions, code reviews, and technical mentorship within the team

- Document data sources, pipeline logic, and processing decisions for reproducibility and team alignment

Requirements

- 3+ years of experience in data engineering

- Degree in Computer Science, Data Engineering, Computer Engineering, Information Systems, or equivalent technical background

- Solid understanding of the ML training lifecycle and what properties make a dataset suitable for model training

- Familiarity with layered data architecture patterns such as Medallion Architecture (Bronze/Silver/Gold) or Data Mesh

- Proficiency in Python, with focus on data manipulation, pipeline development, and automation

- Workflow orchestration using code-based tools such as Temporal, Airflow, Prefect, Dagster, or equivalent

- Distributed data processing with Spark, Databricks, or similar

- REST and gRPC API integration

- Strong SQL skills, both for data modeling and query optimization

- Experience with streaming systems and event-driven pipelines (Kafka, Kinesis, or equivalent)

Soft Skills

- Comfortable jumping into ongoing codebases and optimizing work built by others, without