Data Engineer, PySpark & ML Infrastructure
Overview
You will build and support ML infrastructure, feature stores, and large-scale data pipelines using PySpark, Python, and SQL. You will join a team focused on data engineering, not model development. Your work will power machine learning workflows for a Google Cloud environment. This role centers on infrastructure and pipeline reliability, offering the chance to shape data systems from day one.
What You'll Do6
- 1Build scalable data pipelines using PySpark and Python to process large datasets
- 2Design and manage feature stores for machine learning applications, ensuring data consistency and availability
- 3Optimize SQL queries and BigQuery workloads for performance and cost efficiency
- 4Ship reliable data ingestion and transformation jobs for ML infrastructure
- 5Debug and resolve pipeline failures, maintaining data quality and timeliness
- 6Collaborate with data scientists to understand feature requirements and deliver robust data solutions
Requirements6
- 15+ years in data engineering with Python and SQL for large-scale data processing
- 23+ years hands-on with PySpark for ETL and data transformation
- 3Experience building and maintaining BigQuery datasets and queries
- 4Track record of developing and managing data pipelines in Google Cloud Platform
- 5Strong understanding of ML infrastructure concepts, including feature stores
- 6Proven ability to write efficient, tested code for production environments
Salary Insight
Salary not disclosed in listing
Location
Required Skills
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