Senior Data Engineer (Rust)

RemoteSeniorData Engineerposted 1mo ago

✦Role summaryAI-generated

This role focuses on architecting high-throughput, low-latency data infrastructure in Rust while integrating with cloud warehouses and lakehouses, requiring deep expertise in scalable distributed systems and resilient API design. The candidate will lead technical direction for data platforms, balancing performance, memory efficiency, and enterprise-grade reliability.

Skills required

Rust Distributed systems Data pipelines Cloud data warehousing Api design Low latency optimization Data infrastructure Data engineering AWS GCP Azure Snowflake BigQuery Databricks Delta Lake Redshift

About this role

We are looking for a Senior 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.

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.

Qualifications

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.