Senior Software Engineer, Data Infrastructure

The Infrastructure team builds and operates the foundations that power Decagon: networking, data, ML serving, developer platform, and real‐time voice. We partner closely with product, data, and ML to deliver high‐scale, low‐latency systems with clear SLOs and great developer ergonomics.

We organize around four focus areas:

Core Infra: The foundational cloud stack—networking, compute, storage, security, and infrastructure‐as‐code—to ensure reliability, scale, and cost efficiency.

Data Infra: Streaming/batch data platforms powering analytics/BI and customer‐facing telemetry, including for customer‐managed and on‐prem environments.

ML Infra: GPU and model‐serving platforms for LLM inference with multi‐provider routing and support for on‐prem/air‐gapped deployments.

Platform (DevEx): CI/CD, paved paths, and core services that make shipping fast, safe, and consistent across teams.

Our mission is to deliver magical support experiences — AI agents working alongside humans to resolve issues quickly and accurately.

About the Role We're hiring a Senior Data Infrastructure Engineer to design, build, and operate the data systems that power Decagon's AI products. You'll own critical data pipelines and storage layers end‐to‐end, improve reliability and performance, and create paved paths that let every Decagon engineer work confidently with data at scale. In this role, you will

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 our analytical data layer — 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: optimize data paths, apply smart caching/partitioning, and hit tight p95/p99 targets.

Lead infrastructure‐as‐code (Terraform) and GitOps practices for data systems; reduce drift with reusable modules and policy‐as‐code.

Participate in on‐call and drive down toil through automation and elimination of recurring data issues.

Your background looks something like this

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.

Even better if you have

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