SAP​/ERP Transformation - Principal Data Engineer

Introduction

Ahold Delhaize USA, a division of global food retailer Ahold Delhaize, is part of the U.S. family of brands, which also includes five leading omnichannel grocery brands – Food Lion, Giant Food, The GIANT Company, Hannaford and Stop & Shop. Ahold Delhaize USA associates support the brands with a wide range of services, including Finance, Legal, Sustainability, Commercial, Digital and E-commerce, Technology and more.

Senior technical authority who designs reference architectures and reusable patterns for data ingestion, transformation, storage, and delivery-embedding automation, observability, and security into every stage.

Serves as the hands-on SAP Transformation Data Conversion Lead, accountable for the engineering approach to extracting, cleansing, harmonizing, transforming, validating, and loading enterprise data into SAP. Partners with data analysts, business data owners, internal engineering teams, and implementation vendors to deliver clean, controlled, auditable ETL pipelines using SAP Business Objects Data Services (BODS) and related platforms.

* Our flexible/hybrid work schedule includes 3 in-person days at one of our core locations and 2 remote days. Our core office location for this role is Salisbury, NC.

* Applicants must be currently authorized to work in the United States on a full-time basis.

• Design reusable patterns for batch and streaming ingestion, transformations, and data product delivery, optimizing for performance and scale.
• Build automation for quality, lineage, and governance checks integrated into CI/CD, including deployment scripts and pipeline templates that reduce manual effort.
• Create and evolve internal tooling, developer platforms, and golden paths that improve reliability and time-to-delivery for data teams.
• Harden security and privacy controls (access, encryption, masking) across platforms and pipelines, integrating controls into automated delivery workflows.
• Elevate reliability with SLOs, telemetry, monitoring/logging/tracing, and automated rollback/reprocessing; contribute to observability implementations (e.g., Data Dog).
• Integrate and optimize cloud services and container orchestration where relevant to improve scalability and reduce operational toil.
• Maintain infrastructure as code and repeatable environments using tools such as Terraform, Ansible, or Cloud Formation.
• Guide solution decisions for complex programs; run technical reviews and threat/quality modeling to ensure robust outcomes.
• Document standards, playbooks, and reusable components to reduce rework and improve consistency across teams.
• Coach teams on data modeling, performance optimization, and design thinking for data consumer experience.
• May be called upon to support critical escalations and must be available during urgent IT incidents as needed.
• Lead end-to-end SAP data conversion across extraction, profiling, cleansing, mapping, harmonization, transformation, validation, reconciliation, and load activities.
• Design, build, test, tune, and maintain SAP BODS jobs, workflows, data stores, reusable transformations, exception handling, and deployment components for mock conversions and production cutover.
• Work with data analysts, SAP functional teams, and business data owners to translate data-object requirements, mappings, and transformation rules into complete, testable ETL specifications.
• Coordinate technical delivery with implementation vendors and consulting partners, including reviewing designs and code, resolving dependencies and defects, and ensuring deliverables meet internal engineering standards.
• Establish automated data-quality controls, reconciliation checks, lineage, audit evidence, monitoring, and exception-management processes to ensure complete and accurate migration results.
• Plan and support conversion rehearsals, mock loads, cutover execution, rollback and reprocessing procedures, post-load validation, and root-cause remediation across legacy systems and SAP.

• Bachelor's degree or equivalent years of work experience.
• 10+ years in data engineering with large scale systems.
• Proficiency in distributed data processing, orchestration, and storage…