OKX · job-boards.greenhouse.io · checked today
Staff Engineer, Machine Learning & Data Infrastructure
Skills, with evidence
- Data modelling
Design and optimize offline data pipelines on Databricks, Apache Spark, and warehouse/lakehouse technologies, addressing data modeling, partitioning, joins, shuffles, skew, incremental processing, and cost.
must have - Incremental loads
Design and optimize offline data pipelines on Databricks, Apache Spark, and warehouse/lakehouse technologies, addressing data modeling, partitioning, joins, shuffles, skew, incremental processing, and cost.
must have - Python
Strong Python, SQL, and at least one backend or systems language such as Java, Scala, Go, or C++.
must have - SQL
Strong Python, SQL, and at least one backend or systems language such as Java, Scala, Go, or C++.
must have - Spark
Deep understanding of Databricks and Spark, or comparable offline data platforms, including execution plans, distributed query optimization, storage layouts, data correctness, and workload cost management.
must have - AWS
Build reusable infrastructure for model packaging, versioning, deployment, serving, monitoring, and rollback across cloud platforms and regions.
must have · not practised here - Azure
Build reusable infrastructure for model packaging, versioning, deployment, serving, monitoring, and rollback across cloud platforms and regions.
must have · not practised here - GCP
Build reusable infrastructure for model packaging, versioning, deployment, serving, monitoring, and rollback across cloud platforms and regions.
must have · not practised here - Cost & performance
Improve model inference latency, throughput, resource utilization, and cost.
- Streaming
You will help teams move models from experimentation into reliable production services, improve model execution performance, and build scalable data foundations for training, evaluation, and real-time decisions.
- Data quality
Experience with the model lifecycle, including training/serving integration, validation, deployment, observability, and rollback.
- Failure handling
Build reusable infrastructure for model packaging, versioning, deployment, serving, monitoring, and rollback across cloud platforms and regions.
- Idempotency & backfills
Connect offline and online feature pipelines, ensuring point-in-time correctness, consistent feature definitions, freshness, lineage, and reproducible backfills.
- Warehousing
Design and optimize offline data pipelines on Databricks, Apache Spark, and warehouse/lakehouse technologies, addressing data modeling, partitioning, joins, shuffles, skew, incremental processing, and cost.
Your plan
- SQL
drills at the Staff level
- Python
drills at the Staff level
- Modelling
drills at the Staff level
- Pipelines
drills at the Staff level
- Spark
drills at the Staff level
The full plan, drill by drill, is on the job page.
leaves in 11 dremoved the moment OKX closes it