Description

You will design, build, and operate reliable data pipelines and cloud-native data platforms for enterprise clients.

This role is on-site.

Responsibilities

• Engineer cloud-native data platforms primarily on AWS, using Databricks, Spark, and distributed processing frameworks.
• Build and maintain ETL/ELT pipelines with Airflow, Kafka, and SQL for high-volume data processing.
• Ensure production readiness by implementing security, testing, observability, and cost-efficiency standards.
• Optimize data performance, reliability, and quality in live environments; troubleshoot and resolve system issues.
• Mentor junior engineers and contribute to technical design discussions, shared standards, and accelerators.

Required Skills

• 10+ years of hands-on data engineering experience with a strong production background.
• Proficiency in Python and Java for data engineering tasks.
• Deep experience with Databricks, Spark, SQL, and Airflow.
• Solid understanding of AWS services, Kafka, and data pipeline architecture.
• Experience with PostgreSQL, Aurora, and distributed processing frameworks.
• Proven track record in architectural and data modeling decisions in enterprise environments.
• Ability to operate at significant scale and complexity with scalable data architectures.

Preferred Skills

• Experience enabling ML, AI, or GenAI workloads through strong data foundations.
• Exposure to modern BI and analytics tooling.
• Experience working in regulated or security-conscious environments.

Key Skills

Education

Any Graduate