AI Data Engineer

Join Spiral as an AI Data Engineer, where you will be responsible for the technical discovery and integration of customer data into our pipeline. This hybrid role combines pre-sale technical scoping with hands-on data-pipeline engineering. You will work closely with customers, design integration approaches, write code for data adaptation, and manage customer relationships post-go-live. The ideal candidate will have a background in computer science or a related field, prior experience with data pipelines, and strong communication skills.

• Conduire des découvertes techniques avec de nouveaux clients pour évaluer les données disponibles et concevoir l'approche d'intégration.
• Écrire le code qui adapte les données de chaque client à notre contrat d'ingestion et le déployer en production.
• Gérer les relations avec les clients après la mise en service, y compris les changements de schéma et les nouvelles demandes de flux de données.
• Clear written communication, async-first, as most coordination happens over email, chat, and tickets across multiple timezones
• 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
• A bias toward decisions over open questions, and a habit of writing the recommendation, not just the options
• Customer-facing communication skills - able to hold a technical conversation with a customer's engineering counterpart and leave with a decision
• Experience with at least one data orchestration or ETL system, whether durable workflow, batch scheduler, or stream processor
• 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 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
• 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
• Strong SQL chops; comfortable querying data to validate and reconcile what landed against what was sent
• Hands-on experience with Temporal or another durable workflow engine
• OAuth integration experience (Salesforce JWT Bearer or Client Credentials especially)
• Experience with a columnar analytics database (ClickHouse, BigQuery, Snowflake, Redshift, DuckDB)
• Infrastructure-as-code experience with AWS CDK or Terraform
• 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