Senior Software Engineer (Streaming Data Pipeline)
You will support Covalent's streaming data pipeline, processing high-volume event streams across many blockchains. You'll bring 5+ years of backend/stream-processing experience alongside proven experience with Kafka and/or Apache Flink. You'll design and own a high-throughput ingestion pipeline for on-chain events with strong delivery guarantees, build stateful streaming jobs, and operate distributed messaging at scale. You'll transform raw node output into a canonical on-chain data model for APIs and analytics, embed robust observability and automated recovery, deploy containerized microservices via modern CI/CD, and contribute through design and code reviews.
Responsibilities
- Design and own a high-throughput ingestion pipeline for on-chain events with strong delivery guarantees
- Build stateful streaming jobs and operate distributed messaging at scale (partitioning, replication, retention)
- Transform raw node output into a canonical on-chain data model for APIs and analytics; integrate external data where needed
- Embed robust observability and automated recovery so systems self-heal under load
- Deploy containerized microservices via modern CI/CD and infrastructure automation in managed cloud environments
- Contribute through design/code reviews; rigorously test and ship secure, scalable software aligned with best practices
Requirements
- Bachelor's degree in CS or related field (or equivalent experience)
- 5+ years in backend/data-infra engineering, including multi-year experience running real-time streaming systems
- Deep experience with distributed streaming and pub-sub platforms; strong grasp of watermarking, checkpointing, back-pressure, and event- vs processing-time
- Proficiency in a statically typed systems language plus strong SQL and API design skills; proven pipeline design and CDC/data-sink experience
- Strength in observability, SLOs, and performance tuning (latency, scalability, availability, efficiency, capacity); ability to optimize reliability and cost at scale
- Familiarity with blockchain data lifecycles, token standards, event ingestion/traces, common data schemas, market-data pipelines, and modern analytical databases