Senior Data Engineer

About Enlighten

At Enlighten, we believe AI should expand what people can take on. We build teams of AI agents alongside Fortune 500 companies so they can put their best thinking to work across the business. We want the people closest to the work to be able to ask hard questions and get answers they can trust. Our mission is to give every team the intelligence it needs to make decisions that shape the business.

About the role

You'll build and own the pipelines behind Enlighten's analytics and AI work. You'll bring data together across systems and resolve inconsistencies so teams can rely on the same datasets and definitions.

What you'll do

• Design and maintain ingestion, transformation, and delivery pipelines with clear contracts and owners.
• Model data for analytical and application users. Document what each row represents, how records join, and how business metrics are defined.
• Catch stale, missing, or duplicate data and upstream schema changes before they affect downstream work.
• Run multiple AI agents in parallel every day to inspect schemas, build pipelines, and trace discrepancies. Create custom apps for inspecting pipeline health and lineage, then review code and reconcile outputs before release.
• Improve orchestration and monitoring so failures are easy to find and pipelines can be rerun safely.
• Work with customer and product teams on access, performance, documentation, and integration patterns other deployments can reuse.

What you bring

• Senior responsibility for data pipelines and models used in production analytical or application workflows.
• Strong SQL and Python skills and experience with APIs, batch processing, and modern cloud data platforms.
• Practical understanding of data modeling, orchestration, schema evolution, and reliable failure recovery.
• Experience validating transformed data against source records and explaining the meaning and limits of a dataset.
• Hands-on use of multiple AI agents and custom data apps, with disciplined review of generated code and transformations.
• The ability to work independently through unfamiliar systems while communicating decisions and risks clearly.
• A habit of leaving useful tests, documentation, and operational ownership with the work you deliver.
• Experience with Snowflake, BigQuery, Databricks, or similar data platforms.
• Experience supporting machine learning, analytical applications, or enterprise customer integrations.
• Experience with semantic models, lineage, or automated data quality checks.
• You work independently. You can take an unclear goal, decide what to do first, find what you need, and carry the work through to a result. You bring people in early when a decision, risk, or dependency needs their input.

How we work

AI agents are core to how we get work done. You'll spend a substantial part of your day running multiple agents in parallel, building tools and workflows with them, and checking what they produce. You own the result. Verify the facts, calculations, code, and claims before they go into a decision, a customer conversation, or a release.

When working with clients, take initiative within the agreed goals, scope, access permissions, and review process. Bring options and recommendations, and involve the right people before changing a customer commitment or production workflow.

What success looks like

• Pipelines deliver usable data with visible freshness, quality, and recovery behavior.
• Analysts and engineers can understand and reuse the models without repeatedly reconciling definitions.
• Data failures are detected, diagnosed, and resolved with a clear record of what changed.
• Agents and custom tools reduce manual work while source reconciliation remains dependable.