Analytics Engineer: Growth & Data Infrastructure

AI Chopping Block; San Francisco, Northern (CA, KY); Hybrid; USD 120,000 - 160,000; Full time

Benefits offered by this job

Lunches in office

Wellness stipend

Flexible vacation policy

Job summary

Decagon is the leading conversational AI platform empowering brands to deliver concierge customer experiences. In this role, you will build 0 to 1 data infrastructure using Python and SQL across multiple databases, parsing engineering and AI logs to construct trustworthy datasets that enable informed decision making.

You will partner with engineering to instrument new product surfaces and ensure clean event and usage data, translate ambiguous business scenarios into measurable data problems

Qualifications

• 3+ years of data experience in an early stage or high growth environment.
• Expert in SQL and BI (Hex, Looker or similar); able to build tables and databases from scratch.
• Owner mentality; end-to-end technologist who can work across data, engineering, and business.
• Nice-to-have: experience with Clickhouse and CDC.
• Experience building custom data connectors and webhooks.
• Optimizing database and query performance.

Responsibilities

• Partner with engineering to instrument new product surfaces and ensure clean, reliable event and usage data.
• Translate ambiguous business scenarios into measurable data problems spanning Growth, GTM, Pricing, Deals Desk, Strategic Finance, and Product.
• Continuously improve data infrastructure as the company and data volumes scale.

Skills

SQL

BI Tools

Data Pipelines

Tools

Clickhouse

Data Connectors

Webhooks

Job description

Decagon is the leading conversational AI platform empowering brands to deliver concierge customer experiences. In this role, you will build 0 to 1 data infrastructure using Python and SQL across multiple databases, parsing engineering and AI logs to construct trustworthy datasets that enable informed decision making.

You will partner with engineering to instrument new product surfaces and ensure clean event and usage data, translate ambiguous business scenarios into measurable data problems