Strong focus on execution, delivery, and customer impact.
Strong expertise in Java and/or Scala; Python experience is a plus.
Experience designing and productionizing large-scale distributed systems with machine-learned models and big data.
Familiarity with ML Ops best practices.
Experience with batch and streaming technologies like Apache Flink, Spark, Beam, Google DataFlow.
Expertise with distributed data stores (Cassandra, Google BigTable, Redis, ClickHouse, Elasticsearch) and messaging systems (Kafka, Google PubSub) at scale.
Experience with Linux, Docker, and cloud services (OpenStack, GCP, AWS, Azure).
Responsibilities:
Build and run production-grade data and machine learning pipelines and products at scale in an agile setup.
Work closely with data scientists, engineers, architects, and product managers.
Contribute to system architecture to enhance scalability and resilience.
Identify opportunities to improve coding, processes, tools, and testing.