Data Engineer/Senior Data Engineer, Data And Science

About the role

The Data Engineering team at Aircall works on providing high-quality, reliable, and actionable data. As an AI-first data team, we are currently in a pivotal transition to build a robust semantic layer that will power our AI-first data platform, enabling analytics at speed and democratizing intelligent insights across the company. Some of the key problems we are currently solving include taking charge of data reliability, integrating new sources for raw data ingestion, and building sophisticated data models to power real-time dashboards and predictive analytics.

In this role, you will be instrumental in building new datasets for high-impact use cases such as churn prediction and feature adoption, while owning the end-to-end reliability and scalability of our data pipelines. You will work closely with Product and GTM business teams, sitting at the heart of a larger data organization alongside Data Science, Analytics, and Applied Scientists to bridge the gap between raw data and AI-driven decision-making.

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

A little more about you:

• 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.
• 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.

Level will be determined by a variety of factors, including years of experience, the scope and complexity of that experience, and interview performance.