Job description

Are you passionate about building scalable data pipelines and optimizing machine learning operations? We are looking for an experienced MLOps / Data Engineer to help design, build, and maintain our risk model infrastructure and data pipelines. Youll play a crucial role in transforming raw data into a structured, optimized format for training and inference, ensuring accuracy, efficiency, and scalability.

What youll do:

Develop and optimize data pipelines for risk model ingestion, processing, and storage.
Build scalable model training, deployment, and monitoring pipelines using cloud technologies (AWS, GCP).
Expose models as pickles for risk service consumption and work closely with the risk team to monitor model performance.
Implement data quality and governance frameworks to ensure accurate and reliable data.
Collaborate with data analysts, scientists, and engineers to deliver insights and improve business decision-making.
Optimize query performance and storage solutions for large-scale datasets.
Research and implement new MLOps best practices to enhance efficiency and scalability.

What were looking for:

3+ years of experience as an MLOps Engineer or Data Engineer (with at least 1 year in the other role).
Proven expertise in designing and implementing data pipelines and systems.
Strong programming skills in Python, Java, or Scala.
Experience with ETL tools (e.g., Apache Airflow, dbt) and relational & NoSQL databases (PostgreSQL, MySQL, Snowflake).
Hands-on experience with cloud-based data solutions (AWS Redshift, Google BigQuery, Snowflake).
Experience with SageMaker and Feature Store (preferred).
Knowledge of distributed data systems (Spark, Kafka) is a plus.
Strong problem-solving skills and ability to work in a fast-paced startup environment.
Excellent communication and collaboration skills to work with both technical and non-technical teams.