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Parloa is an enterprise conversational AI platform that has powered over one billion customer interactions for global brands including Booking.com, HealthEquity, Allianz, and SAP. The company is backed by leading investors like General Catalyst, EQT Ventures, and Altimeter Capital.
As Senior Data Engineer for Internal Platform, you will own the data infrastructure powering Parloa's internal AI transformation. Today, business-critical data is scattered across dozens of disconnected tools with no unified access layer. You will build a self-serve data platform that gives AI agents and cross-functional teams the business context they need to operate effectively.
Key responsibilities include:
**Data Platform Infrastructure**: Own the internal-facing Databricks workspace end-to-end, including configuration, compute resources, access controls, and cost management.
**Ingestion Pipelines**: Build and maintain Airbyte pipelines that automatically pull data from Salesforce, Gong, HRIS, finance systems, and other enterprise tools on scheduled intervals.
**Data Modeling & Transformation**: Design and implement the architecture that converts raw data into clean, trusted, analysis-ready datasets using dimensional modeling principles.
**AI Data Enablement**: Partner with the AI Transformation Team to ensure AI agents and LLM workflows receive data in the optimal format and structure for their consumption patterns.
**AI Adoption Measurement**: Define and build KPIs and dashboards that track AI usage, user proficiency, and business impact across the organization for leadership visibility.
**Data Governance & Compliance**: Own data classification, access policies, GDPR/DPA compliance, and pipeline monitoring to ensure data quality and regulatory adherence.
You should have experience designing data layers for AI/LLM applications and agentic workflows, proficiency in Python and SQL, hands-on experience with ETL/ELT tools and modern data warehouse technologies, and the ability to communicate complex technical concepts to non-technical stakeholders. Understanding how AI tools consume data—context windows, retrieval patterns, embedding pipelines—is essential. You'll work in a high-ownership environment where execution is fast, feedback is direct, and meaningful contribution is expected from day one.
