Description

You will own the platform vision, ensuring systems remain stable under scale while mentoring junior engineers and translating business needs into technical specifications.

This role is on-site.

Responsibilities

• Architect distributed computing frameworks like Apache Spark or Ray for massive-scale parallel data processing.
• Design real-time streaming pipelines using Kafka, Kinesis, or Flink with deep understanding of event-driven architecture.
• Implement data governance, security, and observability, including RBAC, data masking, logging, and alerting mechanisms.
• Build and maintain Directed Acyclic Graphs (DAGs) in Apache Airflow or Prefect for workflow orchestration.
• Apply software engineering principles to data infrastructure using Docker, Kubernetes, and Terraform.

Required Skills

• 5+ years of experience in data engineering or distributed systems.
• Expertise in Python or Scala for reusable pipeline code and API interactions.
• Advanced SQL mastery for metrics computation, window functions, and query performance tuning.
• Hands-on experience with columnar/analytical stores: Snowflake, Redshift, or BigQuery.
• Proficiency with NoSQL databases such as DynamoDB or Cassandra.
• Strong background in public cloud infrastructure, specifically AWS, including storage/compute decoupling and cost optimization.
• Experience with containerization and orchestration tools: Docker and Kubernetes.
• Bachelor's degree in a relevant field.

Preferred Skills

• Familiarity with Java concepts and core Java.
• Experience with streaming engines like Apache Flink.

Key Skills

Education

Bachelor's degree