About Parloa
Parloa’s mission is to make every customer conversation feel effortless for both customers and the companies serving them. As agentic AI accelerates, Parloans are shaping the foundation of a new era in customer experience, one where customer support is no longer transactions, but meaningful exchanges. It is not just a vision; Parloa has powered over ONE BILLION interactions between global enterprise brands and their customers, with companies like Booking.com, HealthEquity, Allianz, SAP, BarmeniaGothaer, and TUI already deploying Parloa at scale.
About the role:
As Lead Data Engineer – Internal Platform, you will build and own the data infrastructure that powers Parloa's internal AI transformation. Today, business-critical data lives across dozens of disconnected tools with no unified way to access it — you'll change that by creating a single, self-serve access layer that gives AI agents and teams the business context they need to operate. Working closely with the AI Transformation Team and stakeholders across the non-engineering organization, you'll also define the KPIs and dashboards leadership uses to track AI adoption and impact.
Areas of ownership:
- Data Platform Infrastructure: Own the Internal-facing Databricks workspace end-to-end: configuration, compute, access controls, and cost management.
- Ingestion Pipelines: Build and maintain Airbyte pipelines that pull data from Salesforce, Gong, HRIS, finance systems, and other tools on automated schedules.
- Data Modeling & Transformation: Implement the architecture that turns raw data into clean, trusted, analysis-ready datasets.
- AI Data Enablement: Partner with the AI Transformation Team to ensure AI agents and LLM workflows get the right data in the right format.
- AI Adoption Measurement: Define and build the KPIs and dashboards that track AI usage, proficiency, and business impact across the company.
- Data Governance & Compliance: Own data classification, access policies, GDPR/DPA compliance, and pipeline monitoring.
Who you are:
- Experience designing data layers that power AI/LLM-based applications and agentic workflows.
- Proficiency in Python, SQL, ETL/ELT tools, and MLOps.
- Knowledge of dimensional modeling and modern data warehouse technologies.
- Ability to comprehend and execute on complex requirements and resolve performance issues.
- Ability to articulate complex technical concepts to non-engineering audiences, making data architecture decisions and trade-offs understandable for leadership and cross-functional stakeholders.
- Passion for staying updated and driving innovation in data engineering.
- Ability to define and instrument Al adoption KPls: usage tracking, proficiency distribution, and business impact metrics.
- Understanding of how Al tools consume data – context windows, retrieval patterns, embedding pipelines – and how to optimise data access for.
Our recruiting process:
Talent Acquisition → Hiring Manager → Technical Interview(s) → Bar Raiser
Why Parloa?
We’re at the beginning of a new era in customer experience, one where AI doesn’t just respond, but understands, reasons, and takes action. We’re building agentic AI that enterprises trust with their most important customer moments: complex questions, high volumes, real stakes. When millions of people reach out to a brand, those interactions aren’t just support tickets; they’re defining experiences. We’re here to raise the standard: making every conversation seamless, intelligent, and genuinely helpful. If you care about shaping how businesses and customers connect at scale and want your work to matter in real, everyday moments, this is where you do it.
At Parloa, ownership isn’t a buzzword; it means being accountable for outcomes, not just tasks. We operate in a category that’s evolving fast, where the bar is high, and the problems are complex. We hire people who think in solutions, communicate with clarity, and follow through. People who are comfortable making decisions, taking responsibility, and raising the standard for themselves and those around them.
We’ve grown beyond the earliest startup phase, but we’ve kept the intensity: fast execution, direct feedback, and a strong expectation to contribute meaningfully from day one.
Backed by leading global investors like General Catalyst, EQT Ventures, and Altimeter Capital, we’re scaling with a clear ambition: to become the global category leader in enterprise-grade conversational AI.
