Data Engineer II

Bachelor’s degree in Computer Science, Engineering, or related field. 2–4 years of experience building and operating large-scale data systems supporting analytics and ML workloads. Proficiency in Python and SQL, with experience in PySpark, pandas, or similar. Experience with DBT. Experience with modern data warehousing and lakehouse platforms, preferably Databricks. Hands-on experience with workflow orchestration tools such as Airflow, Dagster, or Prefect. Strong understanding of data modeling, ETL design, and distributed data systems. Experience with AWS data and compute services (S3, Lambda, ECS, CloudWatch, etc.) or equivalent cloud platforms. Familiarity with MLOps concepts. Experience using Infrastructure as Code, preferably Terraform. Excellent problem-solving, collaboration, and communication skills.

Design, build, and optimize ETL pipelines using Databricks, PySpark, and Airflow. Develop and maintain labeling and retraining pipelines for machine learning models. Implement and support MLOps practices. Collaborate with data scientists to productionize and scale model training, inference, and evaluation pipelines. Contribute to the design and evolution of the data lakehouse. Document and communicate data architecture, lineage, and dependencies. Champion data quality and governance. Leverage infrastructure-as-code and containerization. Participate in code reviews and continuous improvement of engineering best practices.