In this role, you will contribute across the full data lifecycle: ingesting data from production systems, designing and evolving our data warehouse, building batch and streaming pipelines, and making high-quality datasets available to researchers, engineers, analysts, and product teams across the company.
The platform spans tens of terabytes and ingests data from tens of proprietary and third-party sources – including our search engine and its components, CRM, billing, identity, and product analytics across multi-region production environments. Around 100 internal users rely on it daily.
You will work closely with engineers and stakeholders across the company, contribute to architectural and modeling decisions, and help improve the reliability, usability, and scalability of the data platform as it grows.
In this position, your responsibility will be to:
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.
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.
Have 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.
Have deep knowledge of databases, including schema design, query optimization, and familiarity with NoSQL use cases.
Have 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.
Have hands-on experience with Spark, MapReduce, or similar distributed processing systems, and understand when distributed processing is the right tool.
Are fluent in Python and SQL for production data work.
Have 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.
Are comfortable working on ambiguous, cross-functional data problems and collaborating closely with both technical and non-technical stakeholders.
