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Data Engineer (Agentic Search)

Nebius· Agentic Search
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Hybrid (Israel)Full-timePosted 2mo ago

About the role

Data Engineer to build and scale the data platform behind Nebius' agent-native search product, spanning ingestion, data warehouse design, batch and streaming pipelines, and providing high-quality datasets for researchers, engineers, analysts, and product teams.

Responsibilities

  • Contribute to the design, development, and operation of Tavily's data platform — from real-time ingestion through data warehouse medallion layers to consumer-facing datasets and dashboards.
  • Build and maintain reliable batch and streaming pipelines that ingest data from production services and external systems.
  • Design and evolve scalable, analytics-ready data models in the data warehouse.
  • Work closely with engineers across the company to ensure data produced by production systems is reliable, well-structured, and usable downstream.
  • Improve observability across the data platform, including data quality checks, freshness monitoring, lineage, schema evolution, and cost controls.
  • Partner with researchers, engineers, analysts, finance, and product managers to deliver trustworthy datasets for product, search quality, ML, and GTM analytics.
  • Contribute to defining the objects, entities, and relationships that represent Tavily's search domain — including agent inputs, URLs, chunks, agent sessions, crawls, and the connections between them — and translate them into clean, queryable data models.
  • Improve engineering practices around testing, documentation, deployment, and incident response.
  • Investigate and resolve production data issues, including broken pipelines, corrupted datasets, schema changes, and large-scale backfills.
  • Contribute to technical standards and best practices for data engineering across the company.
  • Help maintain high standards of data quality, integrity, security, and governance across environments.

Qualifications

  • Have 5+ years of Data Engineering experience, with strong experience designing and implementing scalable, analytics-ready data models and cloud data warehouses such as Snowflake or BigQuery.
  • Hands-on experience with Snowflake, or a comparable cloud data warehouse, and a strong understanding of modern data warehouse architecture, preferably including medallion-style modeling.
  • Deep knowledge of databases, including schema design, query optimization, and familiarity with NoSQL use cases.
  • Strong experience with modern data orchestration and transformation frameworks such as Airflow and dbt.
  • Understand cloud data services on AWS or GCP and have experience with streaming platforms such as Kafka or Pub/Sub.
  • Hands-on experience with Spark, MapReduce, or similar distributed processing systems, and understand when distributed processing is the right tool.
  • Fluent in Python and SQL for production data work.
  • Operated data systems in production: debugged them under pressure, recovered from data incidents, handled schema changes, and backfilled corrupted or incomplete datasets.
  • Care deeply about data quality and about making datasets understandable and trustworthy for the people using them.
  • Comfortable working on ambiguous, cross-functional data problems and collaborating closely with both technical and non-technical stakeholders.

Skills

Required:Data modelingBatch & streaming data pipelinesData warehouse (Snowflake/BigQuery)SQLPythonAirflowdbt
Nice to have:Kafka/Pub/Sub

Benefits

  • Competitive compensation
  • Career growth and learning opportunities
  • Flexibility and ownership
  • Collaborative and innovative culture
  • Opportunity to work on impactful AI projects
  • International environment and talented teams
Last updated Aug 26, 2026. Interested? Apply on the company site.
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