Data Engineer
Summary
Remote (Athens metro only) Data Engineer on Orca AI's Data Platform team, building a data lake and high-volume batch/streaming ingestion pipelines on Kubernetes (with KEDA), Terraform, Airflow/Prefect, and Snowflake to power analytics, product applications, and large-scale AI model execution.
**This is a remote position for candidates in Athens metropolitan only***
We are looking for a highly skilled Data Engineer to join our Data team and play a key role in designing, building, and evolving the company’s data infrastructure.
This is a hands-on engineering position focused on developing scalable data platforms, high-volume ingestion pipelines, and the foundational data systems that power analytics, product applications, AI workloads, and large-scale model execution. You will help architect our data lake, build reliable ingestion capabilities at scale, and shape the core infrastructure that supports the company’s future growth.
This is an exciting opportunity to work with cutting-edge technologies and have a defining impact on the architecture and evolution of our data services.
What You’ll Do
- Develop and optimize large-scale batch and streaming data pipelines.
- Design and develop data models and a semantic layer that serve as the company’s single source of truth, providing a reliable foundation for backend product features and enabling everyone in the company to easily access and derive insights from data.
- Drive query performance and cloud cost optimization across Snowflake and AWS, continuously monitor Snowflake performance and workloads to identify resource-intensive or inefficient processes that cause slow performance or high costs.
- Establish data quality, observability, and reliability standards - freshness, correctness, and SLAs
- Provide technical guidance for the team, drive best practices around infrastructure, CI/CD, testing, and system design.
Requirements
- 5+ years of experience as a Data Engineer or equivalent role
- Experience building data lake architectures.
- Experience deploying and maintaining containerized services in Kubernetes environments.
- Proven experience designing and operating large-scale data processing workloads on Kubernetes
- Expertise in Spark and distributed data processing at scale.
- Strong proficiency in Python and SQL.
- Experience with managing a data orchestration platform such as Airflow or Prefect
- Experience with Snowflake.
- Experience with real-time streaming (Kafka, Kinesis, or Flink) - advantage