Data Engineer/Senior Data Engineer, Data and Science

Aircall•San Francisco, CA

74d

About The Position

The Data Engineering team at Aircall is responsible for providing high-quality, reliable, and actionable data. As an AI-first data team, we are currently undergoing a significant transition to build a robust semantic layer that will power our AI-first data platform. This platform aims to enable analytics at speed and democratize intelligent insights across the company. Key challenges include ensuring data reliability, integrating new sources for raw data ingestion, and developing sophisticated data models for real-time dashboards and predictive analytics. In this role, you will be crucial in developing new datasets for high-impact use cases like churn prediction and feature adoption, while also taking ownership of the end-to-end reliability and scalability of our data pipelines. You will collaborate closely with Product and GTM business teams, working within a larger data organization that includes Data Science, Analytics, and Applied Scientists to bridge the gap between raw data and AI-driven decision-making.

Requirements

Bachelor's degree or higher in Computer Science, Engineering, or a related field.
3+ years of experience in data engineering, with a strong focus on designing and building data pipelines and infrastructure.
Proficient in SQL and Python, with the ability to translate complexity into efficient code.
Experience with data workflow development and management tools (dbt, Airflow).
Solid understanding of distributed computing principles and experience with cloud-based data platforms such as AWS, GCP, or Azure.
Strong analytical and problem-solving skills, with the ability to effectively troubleshoot complex data issues.
Excellent communication and collaboration skills, with the ability to work effectively in a cross-functional team environment.

Nice To Haves

Prior experience designing AI-ready data semantic layer is a major plus, specifically for enabling low-latency, high-fidelity analytics at scale.
Experience with data tooling, data governance, business intelligence and data privacy is a plus.

Responsibilities

Design, build and maintain core data infrastructure pieces that allow Aircall to support our many data use cases.
Enhance the data stack, lineage monitoring and alerting to prevent incidents and improve data quality.
Implement best practices for data management, storage and security to ensure data integrity and compliance with regulations.
Own the core company data pipeline, responsible for converting business needs to efficient & reliable data pipelines.
Participate in code reviews to ensure code quality and share knowledge.
Lead efforts to evaluate and integrate new technologies and tools to enhance our data infrastructure.
Define and manage evolving data models and data schemas.
Manage SLA for data sets that power our company metrics.
Collaborate with applied scientists, data scientists, analysts and other business stakeholders to drive efficiencies for their work, supporting complex data processing, storage and orchestration.