AI Data Engineer, UJET

• 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

Desirable

• 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
• 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