Data Engineer

About Us

UJET leads the way in AI-powered contact center innovation, delivering a future-proof, cloud platform that redefines the customer experience with cutting-edge AI, true multimodality, and a mobile-first approach. We infuse AI across every aspect of your customer journey and contact center operations, to drive automation and efficiency. UJET’s AI solutions empower agents, optimize customer journeys, and transform contact center operations for elevated experiences and actionable insights.

Built on a cloud-native architecture with a unique CRM-first approach, UJET ensures unmatched security, scalability, and prioritized data insights (without storing PII). Designed for effortless use, UJET partners with businesses to deliver exceptional interactions, smarter decision-making, and accelerated growth in the AI-driven world.

Learn more t.cx.

UJET is looking for a Data Engineer to join our growing Data Platform team. You’ll own our data systems end to end: CDC replication out of production MySQL, the pipelines and jobs that shape data in Big Query, the Terraform-managed infrastructure underneath, and the Looker models on top. dbt is central to how we work. You’ll build models yourself and set the patterns that let analysts, product partners, and engineers ship their own safely.

You should be comfortable debugging a replication issue in the morning and tuning a Looker explore in the afternoon.

This data powers reporting for thousands of customer tenants, including embedded Looker analytics for enterprise customers, and the internal metrics our Operations and Finance teams run the business on. Over the coming year, our focus is data trust and reliability, platform modernization, and treating data as a product. You’ll help shape all three.

• Sources and ingestion: Cloud SQL (MySQL) and Big Query via GCP Datastream (CDC)
• Warehouse and transformation: Big Query, dbt (dbt Labs)
• Orchestration: GKE-based, using Kubernetes Cron Jobs and Argo Workflows
• Semantic layer and reporting: Looker / LookML
• Languages: Python and SQL; core application infra is Ruby on Rails and Go

• Build and evolve the data platform foundations that make it easy and safe for others to ship dbt work (project structure, environments, permissions, patterns, documentation)
• Establish and maintain standards and guardrails for dbt development (testing strategy, source freshness, documentation, code review practices)
• Improve the developer experience for data workflows, including CI/CD, automated checks, and repeatable deployment patterns
• Manage data infrastructure as code with Terraform, including pipelines, IAM, and environments

• Improve and troubleshoot CDC replication from MySQL to Big Query with Datastream
• Build tooling for backfills, drift detection, and reconciliation between source databases and the warehouse
• Contribute to infrastructure decisions on data ingestion and platform evolution, including latency, throughput, and alternative CDC approaches

• Partner with Analytics and Finance to define and deliver trusted metrics and dashboards in Looker
• Build and maintain analytics-ready datasets that support self-serve reporting and experimentation
• Support customer-facing, multi-tenant reporting with a focus on correctness, tenant isolation, and query performance

• Develop and optimize Big Query data models for analytics and product use cases
• Implement ELT best practices in dbt, including testing, documentation, and versioning

• Design, build, and maintain scalable data pipelines using Python, dbt, and containerized jobs on GKE
• Ensure data quality, reliability, and…