About Nagarro Nagarro helps future-proof your business through a forward-thinking, fluidic, and CARING mindset. We excel at digital engineering and help our clients become human-centric, digital-first organizations, augmenting their ability to be responsive, efficient, intimate, creative, and sustainable.

About the Role We are looking for a Senior Rust Data Engineer who can lead the architecture and scaling of high-throughput data services, ingestion pipelines, and resilient API contracts. Leveraging Rust, you will build ultra-low-latency, memory-efficient data infrastructure and integrate it seamlessly with enterprise cloud warehouses and lakehouses.

Key Responsibilities:

• Lead the architecture and scaling of high-throughput data services and ingestion pipelines.
• Build high-performance, low-latency data infrastructure using Rust.
• Design resilient APIs, data contracts, and schemas.
• Integrate data services with cloud warehouses and lakehouse platforms.
• Optimize distributed data processing, storage, caching, and query performance.
• Drive technical architecture and best practices for scalable data platforms.

Requirements:

• 8+ years of experience in software/data engineering.
• Proven background delivering robust, distributed systems and large-scale data platforms in production.
• Production Rust: Strong, hands-on experience building multi-threaded, asynchronous services in Rust, with deep knowledge of memory management and concurrency patterns.
• Contract-Driven API Design: Extensive experience establishing data contracts and schemas using Protobuf/gRPC, Avro, or OpenAPI, including backward compatibility and schema evolution strategies.
• Cloud & Warehouse Mastery: Deep expertise in at least one major cloud provider (AWS, GCP, Azure) and production mastery of at least one enterprise analytical platform:
• Snowflake, Google BigQuery, Databricks (Delta Lake), or AWS Glue/Redshift.
• Distributed Data Fundamentals: Advanced understanding of columnar storage (Parquet), partitioning/clustering, distributed caching, and query engine optimization.

About Nagarro Nagarro helps future-proof your business through a forward-thinking, fluidic, and CARING mindset. We excel at digital engineering and help our clients become human-centric, digital-first organizations, augmenting their ability to be responsive, efficient, intimate, creative, and sustainable.

About the Role We are looking for a Senior Rust Data Engineer who can lead the architecture and scaling of high-throughput data services, ingestion pipelines, and resilient API contracts. Leveraging Rust, you will build ultra-low-latency, memory-efficient data infrastructure and integrate it seamlessly with enterprise cloud warehouses and lakehouses.

Key Responsibilities:

• Lead the architecture and scaling of high-throughput data services and ingestion pipelines.
• Build high-performance, low-latency data infrastructure using Rust.
• Design resilient APIs, data contracts, and schemas.
• Integrate data services with cloud warehouses and lakehouse platforms.
• Optimize distributed data processing, storage, caching, and query performance.
• Drive technical architecture and best practices for scalable data platforms.

Requirements:

• 8+ years of experience in software/data engineering.
• Proven background delivering robust, distributed systems and large-scale data platforms in production.
• Production Rust: Strong, hands-on experience building multi-threaded, asynchronous services in Rust, with deep knowledge of memory management and concurrency patterns.
• Contract-Driven API Design: Extensive experience establishing data contracts and schemas using Protobuf/gRPC, Avro, or OpenAPI, including backward compatibility and schema evolution strategies.
• Cloud & Warehouse Mastery: Deep expertise in at least one major cloud provider (AWS, GCP, Azure) and production mastery of at least one enterprise analytical platform:
• Snowflake, Google BigQuery, Databricks (Delta Lake), or AWS Glue/Redshift.
• Distributed Data Fundamentals: Advanced understanding of columnar storage (Parquet), partitioning/clustering, distributed caching, and query engine optimization.