Compared with the selected benchmark ("All roles in San Francisco, United States"), this listing's salary midpoint is about 87% higher. The offer sits above the benchmark range (€10,707–€16,060). The listed pay band (€16,667–€33,333) is wider than the benchmark, which suggests greater salary variability. This benchmark is based on 3 comparable listings.

Monthly salary comparison for Data Infrastructure Engineer
Market	Lower bound (25th percentile)	Median	Upper bound (75th percentile)
All roles in San Francisco, United States	€10,707/per month	€14,107/per month	€16,060/per month

About the role

Lead data infrastructure at Decagon. Ensure high throughput pipelines, real-time and batch processing, observability, and reliability. Collaborate with research and product teams to scale features. Compensation: $200K–$400K plus equity.

Job Details

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 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.

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.