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NCR Atleos

Data Engineer II

LocationGURGAON, IND
Typefull-time
SeniorityMid
Company size10,000+ people
PostedOct 6, 2026 · 6d ago
Verified live15h ago
At a glanceSummarised by Seekless from the posting.
Build and operate ingestion pipelines from enterprise source systems into the enterprise data lakehouse
Work across multiple ingestion patterns including CDC, orchestrated batch pipelines, event streaming, packaged analytics/replication tooling, and API-based ingestion
Own new-source onboarding end to end
Build and extend medallion architecture (raw, cleansed, curated layers)
Write and optimize SQL/stored-procedure and Spark-based transformations
Model dimensional entities and conformed keys
Build the semantic and reporting layer
Build and extend source-to-target reconciliation frameworks
Implement data accuracy, completeness and freshness/recency metrics per data domain
Investigate and resolve production data disconnects end to end
Tune long-running transformations and reduce compute consumption
Manage schema drift and upstream release changes programmatically
Work within CI/CD and Git-based change control
Support production: monitoring, alerting, incident triage and RCA for pipelines
Automate repetitive processes
Context engineering: assemble what enters each model call
Build harness and scaffolding components around the model
Write automated evals, groundedness checks, and regression tests
Apply guardrails: input/output constraints, permission gates, human-in-the-loop review
Instrument tracing and telemetry for observability
Work cost-aware regarding token and compute spend
Offers of employment are conditional upon passage of screening criteria applicable to the job
NCR Atleos is an equal-opportunity employer
Skills
SQL
Spark
Python
Git
CI/CD
ERP
CRM
ITSM
CDC
API
About NCR Atleos
NCR Atleos, headquartered in Atlanta, is a leader in expanding financial access. Our dedicated 20,000 employees optimize the branch, improve operational efficiency and maximize self-service availability for financial institutions and retailers across the globe.
Key Expectations
Data Engineering — the foundation
•
Ingestion & integration
•
Build and operate ingestion pipelines from enterprise source systems — cloud and on-premise ERP, CRM, ITSM, billing/revenue, planning and quoting platforms, and third-party operational systems — into the enterprise data lakehouse.
•
Work across multiple ingestion patterns — change data capture (CDC), orchestrated batch pipelines, event streaming, packaged analytics/replication tooling, and API-based ingestion — and select the pattern that fits the latency, volume, and cost profile of the use case, including near-real-time ingestion where the business value justifies it.
•
Own new-source onboarding end to end: source system analysis with application and ERP teams, schema and field availability confirmation, ingestion design, historical backfill, incremental load strategy, and exposure through the curated serving layers.
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.
Legal
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.
Statement to Third Party Agencies
To ALL recruitment agencies: NCR Atleos only accepts resumes from agencies on the NCR Atleos preferred supplier list. Please do not forward resumes to our applicant tracking system, NCR Atleos employees, or any NCR Atleos facility. NCR Atleos is not responsible for any fees or charges associated with unsolicited resumes.
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