Senior Data Engineer

POSITION DESCRIPTION

As a Data Engineer at Sayari, you will be the engine behind the world’s most comprehensive commercial world model. You will join a high-autonomy team responsible for building and scaling the complex orchestration systems that transform billions of primary-source records into actionable intelligence. This is a role for a "builder" who respects the complexity of large-scale ETL and graph databases and is "PhD-curious" about the future of AI-native data products and modern orchestration.

JOB RESPONSIBILITIES

• Design, build, and maintain scalable data pipelines using Python, Spark, and Airflow to support our core data acquisition and entity resolution engines.
• Collaborate cross-functionally with AI/ML and Product teams to implement new features and AI-native products.
• Proactively identify and resolve bottlenecks in our complex ETL processes, bringing a fresh perspective to refine and optimize our existing codebase.
• Contribute to a robust engineering culture through rigorous code reviews, unit testing, and clear communication of design decisions.
• Own the end-to-end delivery of roadmap tasks within two-week sprints, ensuring work meets high standards for quality, documentation, and performance.
• Participate in roadmap planning and story refinement, eventually taking ownership of major epics that drive our long-term product defensibility.

SKILLS & EXPERIENCE

Required

• 5 or more years of production data engineering experience, with clear ownership of systems you built and operated end to end
• Strong Python, with meaningful experience in a JVM language (Scala preferred) or willingness to ramp quickly
• Hands-on Snowflake experience, or equivalent depth in BigQuery or Redshift with demonstrated ability to transfer
• Experience deploying and operating AI or ML applications in production, including output validation, monitoring, and cost management at scale
• Orchestration experience with Apache Airflow or a comparable workflow tool
• Track record of operating production systems reliably, with comfort navigating failure, monitoring, and recovery

Preferred

• Experience with Spark on Dataproc Serverless or other serverless Spark environments
• Familiarity with Kubernetes for deployment
• Experience with data quality tooling such as deequ, Great Expectations, or equivalent
• GCP experience (BigQuery, Dataproc, Cloud Storage)
• Experience leading or contributing to a data warehouse migration
• Background in team mergers or migrating a team onto a new operating process