Data Engineer

Role overview

Design and operate data systems that support customer-facing application features as well as shared analytics foundations. You’ll work with software engineers and product partners to shape data models, integrations, and service interfaces, while making pipelines secure, dependable, and useful to downstream teams. The work spans architecture and implementation, including how systems handle change, failures, access boundaries, and data lifecycle requirements.

Responsibilities - Translate application needs into data models, pipeline designs, and clear interfaces between systems. - Build and operate production pipelines using SQL, Python, PySpark, and cloud data platforms, integrating APIs, databases, and varied data formats. - Create ingestion and transformation workflows for application services and downstream consumers. - Preserve tenant boundaries, source permissions, regional constraints, and requirements for updates and deletion. - Make pipelines recoverable with incremental processing, checkpoints, retries, backfills, replay, and data-quality checks. - Add monitoring for freshness, completeness, progress, and failures; contribute to design reviews, testing, documentation, and deployment practices.

Requirements - At least five years of relevant engineering experience, including production data pipelines and architecture contributions. - Strong SQL and Python skills, with relational database, data modeling, and query optimization experience. - Experience integrating external APIs and handling evolving schemas, imperfect source data, and incremental updates. - Familiarity with engineering practices for data systems, including version control, automated tests, code review, CI/CD, logging, and monitoring. - Experience with Azure or GCP and a cloud data platform such as Databricks, Spark, or Delta Lake, or comparable tools. - Ability to investigate issues across the data lifecycle, explain trade-offs, collaborate across disciplines, and work independently on a distributed team.

Nice to have - Data systems for customer-facing or multi-tenant products, or integrations with third-party services. - Document processing, knowledge systems, or data pipelines supporting AI features. - Data access controls, governance, regional requirements, or retention and deletion policies. - Event-driven processing, infrastructure as code, or cloud workload cost optimization.

Benefits and work setup - Remote-first workplace with hiring hubs in the US, Canada, UK, and Germany. - Paid vacation, parental and family care leave, and sick leave. - Country-specific health benefits, life insurance, home internet stipend, home-office setup allowance, and professional development support.