Senior Software Engineer, Data Infrastructure

Decagon•San Francisco, CA

1d•$200,000 - $400,000•Onsite

About The Position

Decagon is seeking a Senior Data Infrastructure Engineer to design, build, and operate the data systems that power its AI products. The role involves owning critical data pipelines and storage layers end-to-end, enhancing reliability and performance, and establishing paved paths for engineers to work with data at scale. The Infrastructure team at Decagon builds and operates the foundational systems for networking, data, ML serving, developer platform, and real-time voice, partnering with product, data, and ML teams to deliver high-scale, low-latency systems with clear SLOs and excellent developer ergonomics. The team is organized around four focus areas: Core Infra, Data Infra, ML Infra, and Platform (DevEx).

Requirements

5+ years building and operating production data infrastructure at scale.
Hands-on experience with Tier 1 data technologies: ClickHouse, Kafka (or MSK/Pub-Sub/RabbitMQ), and Flink or dbt.
Proven track record meeting high availability and low latency targets across streaming and batch workloads.
Excellent observability chops (OpenTelemetry, Prometheus/Grafana, Datadog) and strong incident response discipline.
Clear written communication and the ability to turn ambiguous data requirements into simple, reliable designs.

Nice To Haves

Experience with CDC tooling (Debezium) and orchestration frameworks (Airflow, Dagster, or Prefect)
Familiarity with Spark or Dask for large-scale data processing
Experience with cloud data warehouses (Snowflake, BigQuery, Redshift, Databricks)
Experience being an early data/platform/infrastructure engineer at another company
Strong Kubernetes experience (GKE/EKS/AKS) and multi-cloud exposure (GCP, AWS, Azure)
Experience with customer-managed deployments

Responsibilities

Design and implement high-throughput data pipelines and streaming systems with strong SLOs, clear runbooks, and actionable telemetry.
Build and operate real-time and batch ingestion infrastructure using tools like Kafka, Flink, and Airflow.
Own the analytical data layer, including schema design, query performance, and cost optimization across ClickHouse, BigQuery, or similar.
Partner with research and product teams to architect data solutions, evaluate performance, and scale new features.
Tune pipeline and query latencies by optimizing data paths, applying smart caching/partitioning, and hitting tight p95/p99 targets.
Lead infrastructure-as-code (Terraform) and GitOps practices for data systems, reducing drift with reusable modules and policy-as-code.
Participate in on-call rotations and drive down toil through automation and elimination of recurring data issues.