Senior Data Engineer

Role 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.

Qualifications

• 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.

Benefits

• 100% fully paid medical, vision, and dental for employees and their dependents.
• Generous time off; we observe all US federal holidays, close our office for a winter break (12/24-12/31), in addition to granting 18 PTO days and 10 sick days.
• Outstanding compensation package; competitive commissions for revenue roles and bonuses for non-revenue positions.
• A strong commitment to diversity, equity, and inclusion.
• Eligibility to participate in additional benefits such as 401k match up to 5%, 100% paid life insurance (up to $100,000 coverage), and parental leave.
• A collaborative and positive culture - your team will be as smart and driven as you.
• Limitless growth and learning opportunities.

Pay Range

$140,000 - $160,000 USD