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Orum is an AI-powered sales enablement platform that helps sales teams connect faster, sell smarter, and grow revenue through intelligent dialing, real-time conversation insights, AI-driven coaching, and virtual sales floors. The company is building the next generation of its data platform to support customer-facing analytics, internal business intelligence, product decision-making, and future AI-powered products.
As Staff/Principal Data Engineer, you will own Orum's data platform ecosystem end-to-end, serving as a foundational data hire with broad responsibility across data engineering (~70%) and analytics/BI (~30%). You'll architect and maintain a modern analytics stack powered by ClickHouse, BigQuery, dbt, and Looker, working closely with Engineering, Product, Finance, Go-to-Market, and Operations.
Key responsibilities include: defining and driving the data platform architecture and technical roadmap; building and maintaining scalable ETL/ELT pipelines from PostgreSQL, application systems, and event streams; designing robust dbt transformation layers and reusable data models; owning the reliability, performance, cost, and scalability of ClickHouse and BigQuery workloads; building real-time and batch data pipelines for analytics, product, and AI use cases including large-scale event, call, transcript, and conversational data; establishing standards for data quality, testing, lineage, governance, and observability; owning the Looker environment end-to-end including LookML models, explores, dashboards, permissions, and performance; developing a trusted semantic layer with consistent metric and dimension definitions; partnering with cross-functional teams to translate business questions into scalable data models and self-service analytics; and supporting high-impact ad hoc analysis.
Required qualifications: 8+ years in Data Engineering, Analytics Engineering, Platform Engineering, or related roles; deep expertise in SQL, data modeling, dimensional modeling, and analytics-oriented schema design; hands-on experience with dbt and modern ELT architectures; strong experience with ClickHouse and BigQuery; production experience owning or administering Looker including LookML development and performance optimization; experience with streaming technologies such as Kafka or Pub/Sub; strong understanding of cloud infrastructure, distributed systems, and data platform architecture; ability to operate with high ownership, drive ambiguous initiatives independently, and communicate complex data concepts to technical and non-technical audiences.
Bonus qualifications include deep ClickHouse experience at scale, Looker implementation ownership or migration experience, product analytics or customer-facing analytics platform experience, familiarity with LLM infrastructure, embeddings, RAG systems or AI data pipelines, experience with conversational, telephony, or large-scale unstructured datasets, and experience in high-growth startup environments.
