AI Data Engineer

UJET•Los Angeles, CA

7h•$80,000 - $100,000•Hybrid

About The Position

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. UJET’s conversation-analytics product, Spiral, is helping product, support, and CX leaders see what their customers are actually saying at scale. We ingest voice recordings, chat transcripts, surveys, and ticket data from every CCaaS, CRM, and storage system our customers can hand us, transcribe and PII-redact at scale, and use LLMs to surface user stories, taxonomies, and the trends that drive product and operational decisions. We are hiring an AI Data Engineer to own the seam between "customer says yes" and "data is flowing into Spiral." You will run technical discovery with new customers, design the integration that brings their data into our pipeline, and then build it end-to-end, writing the code that adapts their data, wiring the cloud infrastructure that hosts the connection, and shipping the result into production. This is a hybrid role that combines pre-sale technical scoping with hands-on data-pipeline engineering. You will be the customer's technical counterpart from the first discovery call through steady-state ingest. You will be a member of the Spiral engineering team in every operational sense - working in a shared codebase, opening pull requests, participating in code review, debugging in production, and shipping changes through CI alongside the rest of engineering. This role is engineering-team membership with a customer-facing dimension; it is not a customer-success or pre-sales role that occasionally touches code.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, Data Science, or Data Analytics with a CS/computing focus - or an equivalent rigorous technical degree
Prior production engineering experience with data pipelines, integrations, or analytics infrastructure - internship, contract, or full-time all qualify
Daily fluency with a standard engineering workflow: working in a shared codebase via git, authoring pull requests, giving and receiving substantive code review, debugging from logs, and shipping through CI - this is everyday work in this role, not a once-a-quarter activity
Comfort using AI as part of the everyday engineering workflow - LLM APIs, AI-assisted coding tools, prompt design, and a clear-eyed view of where AI multiplies the work and where its first pass needs to be overridden
Comfort writing production code in a modern typed or scripting language (years of experience scaling with level - new grads with strong fundamentals welcome)
Working knowledge of a major cloud provider - identity and access management, object storage, secrets handling, and the security model that governs cross-organization access
Experience with at least one data orchestration or ETL system, whether durable workflow, batch scheduler, or stream processor
Strong SQL chops; comfortable querying data to validate and reconcile what landed against what was sent
Customer-facing communication skills - able to hold a technical conversation with a customer's engineering counterpart and leave with a decision
Clear written communication, async-first, as most coordination happens over email, chat, and tickets across multiple timezones
A bias toward decisions over open questions, and a habit of writing the recommendation, not just the options

Nice To Haves

Hands-on experience with Temporal or another durable workflow engine
OAuth integration experience (Salesforce JWT Bearer or Client Credentials especially)
Infrastructure-as-code experience with AWS CDK or Terraform
Experience with a columnar analytics database (ClickHouse, BigQuery, Snowflake, Redshift, DuckDB)
Background in contact center, CCaaS, or CX domains (UJET, Genesys, Five9, Twilio, Aircall, Talkdesk, NICE)
Comfort estimating cost and runtime of transcription or LLM pipelines at six-figure-file scale
Experience building with LLM APIs (Anthropic, OpenAI, AWS Bedrock, or equivalent) - evaluation, prompt design, cost and latency tradeoffs
Has used AI coding agents on a non-trivial engineering project and can articulate where the agent multiplied them and where they had to override its first pass
Prior experience as a Forward-Deployed Engineer, Solutions Engineer, Implementation Engineer, or Technical Account Manager

Responsibilities

Run technical discovery with new customers to scope what data is available, where it lives, and how it will reach Spiral
Design the integration approach for each customer: whether that's cloud-to-cloud bucket access, vendor push uploads, scheduled API pulls, file drops, or auth-protected exports and explain the tradeoffs to their engineering counterpart
Shepherd credential and access exchange between the customer and Spiral - cloud access policies, OAuth or token-based authentication setup, key handoff - and stay on the thread until access is verified end-to-end
Write the code that adapts each customer's data shape into our ingestion contract, and ship it to production through code review
Make infrastructure-as-code changes to provision the cloud resources, identity grants, and secrets each new customer requires
Configure and operate the orchestration that runs each customer's recurring ingest; validate end-to-end through run history, logs, and metrics
Reconcile ingested data against source-system counts; diagnose and backfill historical gaps
Own customer relationships post-go-live: schema changes, new feed requests, data-format drift, and surfacing upsell opportunities back to the account team
Partner with Sales, Customer Success, and Spiral Engineering to compress time-to-first-data on every new deal