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UJET

cloud-native CCaaS with AI and WFM

Data Engineer

Data EngineeringConfirmed live in the last 24 hours
Full-time$140k - $160k /yr

About the Job

Requirements

  • At least 5 years of software engineering experience, including deep data platform experience.
  • Strong programming skills in Python.
  • Strong SQL skills and experience with analytical data modeling.
  • Hands-on experience with BigQuery or a similar cloud data warehouse.
  • Production experience with dbt, including building models.
  • Experience building or improving infrastructure around data workflows, including reliability, observability, CI/CD, permissions, environments, and deployment patterns.
  • Experience with infrastructure as code, preferably Terraform.
  • Experience with a major cloud platform; GCP is preferred.
  • Strong software engineering fundamentals, including testing, version control, and code reviews.
  • Periodic in-person collaboration at a UJET office or designated location is required.

Responsibilities

  • Build and evolve data platform foundations that enable others to deliver dbt work safely, including project structure, environments, permissions, patterns, and documentation.
  • Establish and maintain standards and guardrails for dbt development, including testing strategy, source freshness, documentation, and code review practices.
  • Improve the developer experience for data workflows through 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 BigQuery with Datastream.
  • Build tooling for backfills, drift detection, and reconciliation between source databases and the warehouse.
  • Contribute to infrastructure decisions about 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-service reporting and experimentation.
  • Support customer-facing, multi-tenant reporting, focusing on correctness, tenant isolation, and query performance.
  • Develop and optimize BigQuery 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 observability for critical datasets and reporting.
  • Optimize performance and cost across BigQuery and data pipelines.
  • Integrate data workflows with backend services and APIs.
  • Work with Product and Engineering to translate business needs into data solutions.

Desired Qualifications

  • Experience with GCP services, including Datastream, Cloud Storage, Cloud Run, and Pub/Sub.
  • Change data capture experience with GCP Datastream or MySQL binlog/GTID replication.
  • Experience with Kubernetes-based orchestration, such as Argo Workflows or similar.
  • Experience with Looker, including LookML, semantic modeling, and dashboards.
  • Familiarity with Ruby on Rails or Go, which power the application-layer services the data platform depends on.
  • Experience collaborating with Product to gather requirements and translate business needs into data solutions.

About the Company

UJET

cloud-native CCaaS with AI and WFM

UJET provides a cloud-based customer service platform that combines contact center tooling with workforce management (CCaaS and WFM) and is powered by artificial intelligence. Its system helps organizations run their customer support by offering AI-powered self-service options, guidance for agents, and automated processes. It works as a cloud-native platform that can connect with other business tools like CRMs and IVRs, and it is sold on a subscription per agent per month basis. Compared with competitors, UJET emphasizes an integrated, AI-assisted experience for both customers and agents in a single platform, with a broad set of integrations and a straightforward per-seat pricing model. The company's goal is to help organizations of any size deliver better customer experiences by streamlining and improving how they handle interactions and workforce operations.

Company size
201-500
Company stage
Series D
Total funding
$183.2M
Headquarters
San Francisco, CA
Founded
2015

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