About this role
We are looking for someone with advanced Python programming skills who applies robust software engineering principles to data problems. You will collaborate closely with Data Scientists, ML Engineers, and Product Managers to build the scalable, automated pipelines required to train, deploy, and monitor machine learning models in production.
Required Qualifications
- Degree: Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related technical field (or equivalent practical experience).
- Experience: 4 to 6 years of professional experience in Data Engineering, Software Engineering, or a closely related field.
- Advanced Python: Deep expertise in Python programming. You should be highly comfortable with: Data processing and ML-adjacent libraries (e.g., PySpark, Pandas, NumPy).
- API development
- Writing efficient and production-grade code.
- GCP Mastery: Proven, hands-on experience designing and operating data architectures on Google Cloud Platform. Must have strong experience with: BigQuery (advanced SQL, architecture, and optimization).
- Google Cloud Storage (GCS) .
- Compute/Serverless (Cloud Functions, Cloud Run).
- AI/ML Acumen: Experience working alongside Data Science teams. A strong understanding of the ML lifecycle, feature engineering, and the data requirements for model training and deployment.
- MLOps: Understanding of MLOps principles, model registry, and continuous training pipelines.
Preferred Qualifications
- Vertex AI: Direct experience interacting with or deploying pipelines using Google Cloud's Vertex AI platform.
- Streaming Technologies: Familiarity with real-time data processing using Google Cloud Pub/Sub and streaming Dataflow jobs.
- Infrastructure as Code: Experience managing GCP resources using Terraform.
- Containerization: Proficiency with Docker.