Senior Data Engineer I
Summary
Build and scale ML Ops pipelines in Scala/Python, automate model deployment with Spark/Ray, and optimize cloud costs on AWS/GCP/Azure.
Qualifications
- Masters degree in Software Engineering, Data Engineering, Computer Science or related field
- 5 years of relevant work experience
- Strong Scala and Python background
- Experience with Apache Spark and/or Ray
- Knowledge of AWS, GCP, Azure, or other cloud platform
- Knowledge of current principles and frameworks for ML Ops
- Experience with ML Ops technologies such as ML Flow, DVC, Grafana, DataHub, Databricks
- Experience with machine learning technologies such as PyTorch, TensorFlow, AWS Sagemaker
- Experience with CI/CD pipelines, including Jenkins or Git Actions
- Experience with Docker containerization or Kubernetes orchestration
- Experience in improving data security and privacy, and managing and reducing cloud costs
- Knowledge of API development and machine learning deployment
Responsibilities
- Develop and implement a strategy for continuous improvement of our Machine Learning Ops including versioning, testing, automation, reproducibility, deployment, monitoring, and data privacy
- Develop and report on ML Ops metrics such as deployment frequency, lead time for changes, mean time to restore, and change failure rate
- Collaborate with data scientists, data engineers, API engineers, and the dev ops team
- Build scalable data ingestion and machine learning inference pipelines
- Scale up production systems to handle increased demand from new products, features, and users
- Provide visibility into the health of our data platform (comprehensive view of data flow, resources usage, data lineage, etc) and optimize cloud costs
- Automate and handle the life-cycle of the systems and platforms that process our data