Senior Data Engineer - Search Platform

Role Overview

Nasuni is building the next generation of enterprise search across massive volumes of unstructured data. As a Senior Data Engineer on our Search Platform team, you will own the data ingestion and transformation systems that power search at global scale.

Key Responsibilities

• Design, develop, and operate production-grade ETL/ELT services, primarily in Java, that transform source data into optimized OpenSearch schemas
• Solve large-scale indexing challenges in a multi-tenant, multi-region, multi-cloud SaaS environment
• Build and maintain high-throughput ingestion services using batch and streaming patterns
• Implement monitoring, alerting, and logging to ensure data completeness, accuracy, and operational stability
• Partner with Search leadership on index lifecycle management, re-indexing strategies, and data retention policies
• Build and expose APIs that integrate search ingestion services into Nasuni’s product ecosystem
• Collaborate with Search Quality Engineering to support relevance tuning, experimentation, and A/B testing
• Continuously optimize pipelines for performance, cost efficiency, and scalability

Qualifications

Must-Have Qualifications

• 5+ years of professional software engineering experience
• 2+ years owning production data ingestion pipelines for search or similar systems
• Strong proficiency in Java for building high-throughput backend services
• Hands-on experience with OpenSearch, Elasticsearch, or Solr indexing and mappings
• Advanced SQL skills and strong data modeling fundamentals
• Experience operating data pipelines in production with monitoring and alerting

Preferred Qualifications

• Experience with workflow orchestration tools such as Airflow, Dagster, or similar
• Experience with streaming data platforms (Kafka, Kinesis, or equivalent)
• Python experience for scripting, data validation, or tooling integration
• Familiarity with multi-tenant SaaS architectures

Ideal Qualifications

• Direct experience scaling search ingestion for large datasets
• Experience supporting search relevance initiatives or experimentation frameworks
• Background in cost optimization for high-volume data platforms