Description

Design and implement scalable cloud data architectures across AWS, Azure, and GCP using Databricks and Apache Spark.

This role is hybrid.

Responsibilities

• Design and standardize cloud data architecture patterns for batch and streaming data processing.
• Lead ETL/ELT solution design, including data ingestion, transformation, orchestration, and quality controls.
• Architect scalable, cost-aware, and performance-optimized data pipelines across AWS, Azure, and GCP.
• Define data governance and operational best practices, including lineage, monitoring, and failure handling.
• Collaborate with engineering teams to translate architecture into reusable components and deployment-ready implementations.

Required Skills

• 10+ years of experience in data architecture and cloud data engineering.
• Deep expertise in Databricks and Apache Spark for building and optimizing data processing workloads.
• Cloud data engineering proficiency across AWS, Azure, and GCP.
• Strong experience in ETL and ELT design for reliable transformations and data preparation.
• Proven track record of designing production-grade data pipelines with strong performance and operational considerations.
• Bachelor's or Master's degree in Computer Science or related field.

Key Skills

Education

Any Graduate