Level

Decagon

Senior Software Engineer, Data Infrastructure

Decagon is hiring a Senior Software Engineer, Data Infrastructure in San Francisco, United States. It pays $200k-$400k a year and Level rates it ; you can apply on Decagon.

Level's read on this job

  • On Level's scale, this software engineer role is Works on AI ●●●○: the daily work is on AI products, without building the model.
  • The listed pay starts 19% above the typical software engineer posting on Level.
  • Tools and skills the posting cites include Databricks, Python and Kubernetes.
  • Level classifies it as a senior role.
  • Level lists 133 other open roles at Decagon.

AI in this role

Design and operate high-throughput data pipelines and infrastructure powering conversational AI products.

databrickspythonkubernetessparkkafka
ai-agentsdata-pipelinesstreamingdata-infrastructuredistributed-systems

About Decagon

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.

About the 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:

  • 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

 

Compensation

$200K – $400K + Offers Equity

This range reflects the expected compensation for this role. Compensation within the range is determined based on experience, skills, and the scope of responsibilities, with flexibility for candidates who demonstrate exceptional impact.

In addition to base salary, we offer competitive equity. Final compensation may vary based on location within the United States.

Benefits

We proudly offer the following benefits for our full-time employees:

  • Medical, Dental, and Vision benefits for you and your family

  • Life Insurance and Disability Benefits

  • Retirement Plan (e.g., 401K, pension)

  • Parental Leave

  • Fertility and family building benefits through Carrot

  • Monthly stipend to support your wellness, lifestyle, and work-life balance

  • Daily lunches and snacks in the office to keep you at your best

  • Take what you need vacation policy (subject to local requirements; UK employees receive 25 days of statutory leave)

These benefits are described in more detail in Decagon’s policies, may vary by location, and can change at any time according to applicable compensation and benefits plans.

How we rate this

Senior Software Engineer, Data Infrastructure at Decagon rates 60 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI agentsData PipelinesStreamingData InfrastructureDistributed SystemsDatabricksPythonKubernetes

Also common in software engineer jobs at this level

Pay

This job starts at
$200k
Typical software engineer job
$168k

This posting starts 19% above the typical software engineer job. See software engineer jobs

Questions you could be asked

  1. How do you decide when an AI agent can act on its own versus asking for approval first?
  2. Tell me about a project where data pipelines was part of your work. What did you do?
  3. Tell me about a project where streaming was part of your work. What did you do?
  4. Tell me about a project where data infrastructure was part of your work. What did you do?
  5. Tell me about a project where distributed systems was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: AI agents, Data Pipelines, Streaming, Data Infrastructure, and Distributed Systems. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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