Senior Software Engineer, Data Infrastructure

Decagon is the leading conversational AI platform empowering every brand to deliver concierge customer experiences.

Our technology enables industry-defining enterprises like Avis Budget Group, Block’s Cash App and Square, Chime, Oura Health, and Hunter Douglas to deploy AI agents that power personalized, deeply satisfying interactions across voice, chat, email, SMS, and every other channel.

We’re building a future where customer experiences are being redefined from support tickets and hold music to faster resolutions, richer conversations, and deeper relationships. We’re proud to be backed by world-class investors who share that vision, including a16z, Accel, Bain Capital Ventures, Coatue, and Index Ventures, along with many others.

We’re an in-office company, driven by a shared commitment to excellence and velocity. Our values — Just Get It Done, Invent What Customers Want, Winner’s Mindset, and The Polymath Principle — shape how we work and grow as a team.

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:

• 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.

• 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.

• 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