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Full job description
About the Company: Netomi is the leading agentic AI platform for enterprise customer experience. We work with the largest global brands like Delta Airlines, MetLife, United, and others to enable agentic automation at scale across the entire customer journey. Our no-code platform delivers the fastest time to market, lowest total cost of ownership, and simple, scalable management of AI agents for any CX use case. Backed by WndrCo, Y Combinator, and Index Ventures, we help enterprises drive efficiency, lower costs, and deliver higher quality customer experiences.
Want to be part of the AI revolution and transform how the world’s largest global brands do business? Join us!
Architect and implement scalable, secure, and reliable data pipelines using modern data platforms (e.g., Spark, Databricks, Airflow, Snowflake , etc.). Develop ETL/ELT processes to ingest data from various structured and unstructured sources. Perform Exploratory Data Analysis (EDA) to uncover trends, validate data integrity, and derive insights that inform data product development and business decisions. Collaborate closely with data scientists, analysts, and software engineers to design data models that support high-quality analytics and real-time insights. Write clean, maintainable code with comprehensive unit and integration tests to ensure reliability and stability in Python . Thrive in an agile, collaborative environment and take ownership of end-to-end feature delivery.
Bachelor's or Master's degree in Computer Science, Engineering, or a related field. 2+ 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 (e.g., Apache Airflow, Luigi, Prefect). Strong experience with cloud-based data platforms (e.g., 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, containerization with Docker/Kubernetes). Exposure to AI/ML-integrated solutions or interest in working alongside data science teams. Knowledge of data security and privacy regulations (e.g., GDPR, HIPAA). Familiarity with prompt engineering and how LLM-based systems interact with data.
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