Senior data engineer

Who we are

At Frontiers, our purpose is simple yet ambitious: to make science open. We believe open science empowers the global scientific community to accelerate discovery and develop the solutions needed for healthy lives on a healthy planet.

We are one of the world’s largest and most influential open-access research publishers. Every article we publish is peer-reviewed and quality-certified, ensuring research is accessible to everyone, everywhere. To date, Frontiers research has been viewed over 4 billion times, demonstrating the real-world impact of science without barriers.

Joining Frontiers means being part of a global, mission-driven organization at the intersection of science, technology, and innovation — working alongside passionate colleagues who care deeply about advancing knowledge for the benefit of society.

To learn more about our impact and culture, please watch this video:

https://www.youtube.com/watch?v=jLJ7ZO3wOW4

About the Role: 

• Own and evolve the company’s data infrastructure: event tracking, ingestion pipelines, and analytics tooling
• Manage data collection pipelines via Snowplow, GTM (server-side and client-side), and Airflow-orchestrated workflows running on Cloud Composer
• Build and maintain BigQuery datasets: partitioning, clustering, cost optimization, scheduled queries, and access controls
• Write and maintain Python scripts for data ingestion, transformation, and reporting
• Manage Cloud Storage (GCS) for data staging, Snowplow enriched event storage, and pipeline artifacts
• Configure and maintain GCP IAM, service accounts, and access controls across services and external APIs
• Monitor and debug pipelines using Cloud Logging & Monitoring; set up alerting and ensure pipeline observability
• Work with relational and non-relational databases: schema design, query optimization, performance tuning
• Collaborate with frontend, QA, DevOps, and product teams
• Participate in code reviews and uphold code quality standards
• Contribute to technical improvements (testing, observability, performance, maintainability)
• Mentor mid-level engineers and contribute to architectural decisions

• Python — hands-on experience writing production scripts and data pipelines
• SQL — strong practical experience: queries, indexes, performance tuning, and analytical workloads
• Google Cloud Platform — working experience across GCP services including:
• BigQuery — partitioning, clustering, cost optimization, scheduled queries, access controls
• Cloud Composer — managed Airflow: DAG deployment, environment configuration, GCP Console monitoring
• Cloud Storage (GCS) — data staging, pipeline artifacts, enriched event storage
• IAM & service accounts — secure access management across GCP services and external APIs
• Cloud Logging & Monitoring — pipeline observability, alerting, and debugging
• Apache Airflow / Cloud Composer — building and maintaining DAGs and data workflows
• Snowplow — event tracking setup and data collection pipelines
• Google Tag Manager — server-side and client-side configuration and management
• Azure Databricks — data processing and analytics workloads
• Understanding of clean code principles (maintainable, testable pipelines and scripts)
• Comfortable with Git and code review workflows

Nice-to-have skills

• C# and .NET Core — backend/API development experience is a plus
• REST APIs — design, implementation, authentication/authorization basics
• SQL Server / T-SQL — experience with Microsoft SQL Server environments
• MongoDB
• Elasticsearch
• RabbitMQ or similar queuing system
• Docker / containers
• GraphQL (building or consuming APIs)
• Background job processing (e.g. Hangfire)
• Observability tools (e.g. New Relic or similar)
• PowerShell scripting
• Agile tooling (e.g. Jira)
• AI-assisted development tools (Cursor, etc.)