Description

You will design and implement scalable data architectures on GCP, including data lakes, warehouses, and real-time processing systems.

Responsibilities

• Develop and optimize ETL/ELT pipelines using Python, SQL, and streaming technologies.
• Model and manage knowledge graphs using Neo4j and GraphQL to capture complex relationships.
• Automate infrastructure and deployments using Terraform, Jenkins, and CI/CD best practices.
• Ensure data quality, governance, and observability through validation, monitoring, and documentation.
• Collaborate with data scientists and ML engineers in Agile teams to deliver data-driven solutions.

Required Skills

• 15+ years of experience in data engineering and distributed systems.
• Expertise in cloud-native architectures, specifically GCP.
• Proficiency in Python and SQL.
• Experience building large-scale ETL/ELT pipelines and real-time streaming with Kafka and Apache Beam.
• Hands-on experience with Neo4j and GraphQL for modeling complex data relationships.
• Knowledge of MLOps practices and integrating pipelines with ML workflows.
• Experience using Terraform and Jenkins for infrastructure deployment.
• Bachelor's degree in Computer Science, Engineering, or a related technical field.

Preferred Skills

• Retail or e-commerce experience in customer segmentation, personalization, or supply chain analytics.

Key Skills

Education

Bachelor's degree