AI-Driven Data Engineer for Real-Time Analytics

Aircall; San Francisco (CA); On-site; USD 130,000 - 200,000; Full time

Benefits offered by this job

Competitive salary package

Health benefits

Career growth

Job summary

Aircall, a unicorn AI-powered customer communications platform, seeks a Data Engineer to build data sets powering churn prediction and feature adoption. You’ll own end-to-end reliability and scalability of data pipelines, collaborating with Product, GTM, Data Science, Analytics, and Applied Scientists to enable real-time dashboards and AI-driven insights.

Join a fast-moving, collaborative team that values ownership, learning, and impact while shaping the data foundation for an AI-first analytics

Qualifications

• Bachelor's degree or higher in Computer Science, Engineering, or related field.
• 3+ years of data engineering experience focused on pipelines and infrastructure.
• Proficient in SQL and Python with ability to translate complexity into efficient code.
• Experience with data workflow tools (dbt, Airflow).
• Knowledge of distributed computing and cloud data platforms (AWS, GCP, Azure).

Responsibilities

• Build new datasets for high-impact use cases such as churn prediction and feature adoption.
• Own end-to-end reliability and scalability of data pipelines.
• Collaborate with Product and GTM teams, bridging data and AI-driven decision-making.

Skills

SQL

Python

Data pipelines

Airflow

Distributed computing

Cloud platforms

Data governance

Real-time analytics

Education

Bachelor's degree in CS/Engineering or related field

Tools

dbt

Airflow

Job description

Aircall, a unicorn AI-powered customer communications platform, seeks a Data Engineer to build data sets powering churn prediction and feature adoption. You’ll own end-to-end reliability and scalability of data pipelines, collaborating with Product, GTM, Data Science, Analytics, and Applied Scientists to enable real-time dashboards and AI-driven insights.

Join a fast-moving, collaborative team that values ownership, learning, and impact while shaping the data foundation for an AI-first analytics