Design, implement, and maintain data pipelines to ingest and transform structured, semi-structured, and unstructured data.
Develop data models that support predictive modeling, scenario analysis, and LLM-based workflows.
Integrate third-party APIs, domain-specific datasets, and global data sources.
Define dataset requirements, metadata standards, and validation processes with ML researchers and technical leads.
Build and maintain curated datasets and evaluation pipelines.
Improve data tooling and automation to enable rapid prototyping and iteration.
Participate in exploratory data analysis to validate assumptions, identify data gaps, surface insights and improve model inputs.
Establish standards for data quality, lineage, governance, and reproducibility.
Build monitoring, validation, and versioning processes.
Collaborate with internal and external partners on secure data access and governance.

3-6 years of experience in data engineering or software engineering with a strong data focus.
Experience designing and implementing pipelines across heterogeneous data sources.
Strong proficiency in Python, SQL, Unix tooling, and Git-based workflows.
Experience working with structured and unstructured data.
Familiarity with ETL frameworks and data warehousing concepts.
Experience with cloud services (preferably AWS and Databricks).
Experience integrating external APIs and third-party datasets.
Strong communication skills and ability to collaborate across technical and business teams.
Ability to validate ideas through fast experimentation and iterative prototyping.

Data Engineer

Key skills