Key skills
PythonScalaAIMLETLData EngineeringSnowflakeBigQueryRedshiftSparkAirflowApache AirflowUnit TestingGCPKubernetesServerlessCloud StorageCommunication
About this role
Sayari is a company focused on providing trustworthy AI solutions for economic security and commercial risk. They are seeking a Senior Data Engineer to build and scale complex orchestration systems that transform vast amounts of primary-source records into actionable intelligence, collaborating with cross-functional teams to enhance AI-native products.
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
Requirements:
- 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
- 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