Own the data lifecycle that powers our AI customer care platform—spanning voice, chat, and email channels.
Design and operate reliable data pipelines using Python and SQL to ingest and transform interaction data from voice and text channels.
Manage the data lifecycle within our core databases: PostgreSQL for structured application data and Elasticsearch for search and retrieval.
Build and maintain scalable dbt projects to transform raw logs into high-quality, governed data models.
Collaborate with stakeholders to define key performance indicators (KPIs) like resolution rates, intent accuracy, and cost-per-interaction.
Build and maintain business-facing dashboards in Looker and internal data applications in Streamlit.
Own the "Retrieval" layer: optimize how data is indexed and queried in Elasticsearch to improve AI response context.
Instrument our data pipelines and AI services with Prometheus and Grafana to monitor data quality, processing latency, and pipeline throughput.

Deep experience writing clean, maintainable code for data processing using Python and SQL.
High proficiency in using AI tools (e.g., Claude Code, Cursor, or other LLMs) to accelerate your coding, debugging, and documentation process.
Expert-level experience with dbt and cloud data warehousing/databases.
Hands-on experience operating and optimizing PostgreSQL and Elasticsearch.
Experience designing and building interactive tools with Streamlit.
Experience using Prometheus and Grafana for application-level, pipeline, and data-quality monitoring.

Data & Analytics Engineer – AI Platform Team

Key skills