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

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.

Data Engineer, Data and Science

Key skills