Job highlights

- Develop high-performance batch and streaming data pipelines
- 3-6 years of experience in Data Platform,Data Engineering,or AI Infrastructure
- Hands-on experience with Spark,Kafka,Airflow,Flink,or similar distributed data technologies
- Experience with ClickHouse,Snowflake,BigQuery,Delta Lake / Iceberg,or contemporary data platforms

Job Summary

What will you drive

Responsibilities

- Build and scale data platforms that power analytics, AI, and real-time decision-making.
- Develop high-performance batch and streaming data pipelines.
- Build reusable data services, feature stores, and AI-ready data infrastructure.
- Design and optimize data lakes, warehouses, and distributed storage systems.
- Enable ML teams with scalable training, inference, and model deployment infrastructure.
- Automate data workflows, infrastructure provisioning, monitoring, and platform operations.
- Improve platform reliability, observability, security, and cost efficiency.
- Partner with Engineering, AI, Product, and Analytics teams to deliver scalable platform capabilities.

Requirements





- 3-6 years of experience in Data Platform, Data Engineering, or AI Infrastructure.
- Strong programming skills in Python, Java, or Scala.
- Hands-on experience with Spark, Kafka, Airflow, Flink, or similar distributed data technologies.
- Experience with ClickHouse, Snowflake, BigQuery, Delta Lake/Iceberg, or modern data platforms.
- Familiarity with LLM infrastructure, vector databases, feature stores, or MLOps is a plus.
- Experience with AWS/GCP/Azure, Docker, Kubernetes, Terraform, and CI/CD.
- Strong understanding of distributed systems, data architecture, observability, and platform scalability.
- Builder mindset with a passion for automation and solving problems at scale.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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