Data Engineer II

Modeling & curation

• Build and extend the medallion architecture — raw landing, cleansed/conformed, and curated business layers — including a conformed star-schema layer of facts and dimensions and a denormalized, query-optimized serving layer consumed by BI tools and downstream applications.
• Write and optimize SQL/stored-procedure and Spark-based transformations; model dimensional entities and conformed keys (customer, supplier, product, order, contract, site) so data joins cleanly across Finance, Sales, Service and Supply Chain domains.
• Build the semantic and reporting layer, landing business definitions, hierarchies and metric logic once in the curated layer with documented ownership — so definitions are reusable and AI-consumable rather than rebuilt inside every tool.

Data quality, reconciliation & observability

• Build and extend source-to-target reconciliation frameworks — record counts, change-timestamp deltas, and critical-column matching — and push beyond count-based recon toward content-level sync validation across replication, packaged-analytics and API-sourced data.
• Implement data accuracy, completeness and freshness/recency metrics per data domain and surface them through the platform's observability layer.
• Investigate and resolve production data disconnects end to end — isolate root cause across source, pipeline and curation; repair or reprocess; and put a control in place so the same failure doesn't recur.

Reliability, performance & cost

• Tune long-running transformations and reduce compute consumption; contribute to platform right-sizing, storage cleanup and cost-optimization work.
• Manage schema drift and upstream release changes — programmatically detect deprecated or altered objects, assess downstream impact, and regression-test pipelines across lower environments before production.
• Work within CI/CD and Git-based change control, with automated test generation and validation rather than manual regression cycles.
• Support production: monitoring, alerting, incident triage and RCA for the pipelines you own, including month-end and quarter-close critical windows.

Automation

• Automate what the team does repeatedly — period-close processes, report generation, reconciliation, environment and admin workflows, infrastructure-as-code, and test generation — rather than absorbing the manual effort.

AI Systems Engineering

• Context engineering: assemble what enters each model call — facts, history, retrieved knowledge, state — favoring signal over volume.
• Harness & scaffolding: build components of the runtime around the model — tool/action interfaces, state handling, retries, error handling, stop conditions.
• Evaluations: write automated evals, groundedness checks, and regression tests so quality regressions are caught before users see them.
• Guardrails: apply input/output constraints, permission gates, and human-in-the-loop review per platform standards.
• Observability: instrument tracing and telemetry; investigate failures instead of retrying blindly.
• Work cost-aware — understand token and compute spend and where it goes.

Offers of employment are conditional upon passage of screening criteria applicable to the job.

EEO Statement
NCR Atleos is an equal-opportunity employer. It is NCR Atleos policy to hire, train, promote, and pay associates based on their job-related qualifications, ability, and performance, without regard to race, color, creed, religion, national origin, citizenship status, sex, sexual orientation, gender identity/expression, pregnancy, marital status, age, mental or physical disability, genetic information, medical condition, military or veteran status, or any other factor protected by law.

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