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Netomi is the leading agentic AI platform for enterprise customer experience, working with global brands like Delta Airlines, MetLife, and United to enable AI agent automation at scale across customer journeys. The company is backed by WndrCo, Y Combinator, and Index Ventures.
As a Data Engineer II, you will architect and implement scalable, secure, and reliable data pipelines using modern platforms such as Spark, Databricks, Airflow, and Snowflake. You'll develop ETL/ELT processes to ingest data from structured and unstructured sources, perform exploratory data analysis to uncover trends and validate data integrity, and derive insights that inform data product development and business decisions.
You will collaborate closely with data scientists, analysts, and software engineers to design data models supporting high-quality analytics and real-time insights. You'll write clean, maintainable Python code with comprehensive unit and integration tests to ensure reliability and stability. The role emphasizes ownership of end-to-end feature delivery in an agile, collaborative environment.
Additional responsibilities include working with DevOps practices, CI/CD pipelines, and containerization technologies. You'll have exposure to AI/ML-integrated solutions and work alongside data science teams. Knowledge of data security and privacy regulations (GDPR, HIPAA) and familiarity with prompt engineering and LLM-based systems are valued.
REQUIREMENTS:
- Bachelor's or Master's degree in Computer Science, Engineering, or related field
- 4+ years of hands-on experience in data engineering or backend software development roles
- Solid understanding of relational databases (RDS, MySQL, PostgreSQL)
- Experience with Apache Kafka or RabbitMQ for building asynchronous, decoupled systems
- Proficiency with Python, SQL, and at least one data pipeline orchestration tool (Apache Airflow, Luigi, Prefect)
- Strong experience with cloud-based data platforms (AWS Redshift, GCP BigQuery, Snowflake, Databricks)
- Deep understanding of data modeling, data warehousing, and distributed systems
- Familiarity with DevOps practices (CI/CD, infrastructure as code, Docker/Kubernetes)
- Exposure to AI/ML-integrated solutions or interest in working with data science teams
- Knowledge of data security and privacy regulations (GDPR, HIPAA)
- Familiarity with prompt engineering and LLM-based systems
