SAP​/ERP Transformation - Lead 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.

Enterprise level technical leader who defines the vision, architecture, and operating model for data platforms and data products, enabling secure, scalable, and reliable data solutions that accelerate analytics, reporting, and machine learning across the business. This hands-on leader acts as a conduit between enterprise data engineering teams and the Transformation organization, remaining highly technical while building frameworks, guiding team direction, architecture, and best practices.

* 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.

• Set the north star data architecture (batch/streaming, lakehouse, MDM, governance) and establish actionable standards, guardrails, and reference implementations.
• Establish product-oriented platform capabilities (self-service ingestion, transformation, orchestration, catalog/lineage, quality) and the internal tooling that enables scalable developer and analyst workflows.
• Define and drive automation standards, including CI/CD pipelines and deployment workflows for data platforms and services.
• Embed security, privacy, and compliance by design, leveraging automation to enforce policies and produce audit-ready evidence.
• Orchestrate reliability and observability across pipelines and platforms (SLOs, cost/performance telemetry, automated remediation), including platform-wide monitoring, logging, and tracing practices (e.g., Data Dog).
• Guide cloud service integration and container/orchestration strategy where applicable, ensuring cost-effective scale and predictable performance.
• Maintain and govern infrastructure as code practices (e.g., Terraform, Ansible, Cloud Formation) to standardize environments, reduce drift, and improve repeatability.
• Prioritize roadmaps and investments using measurable value, risk reduction, and customer (data consumer) outcomes; evaluate and adopt new technologies to improve platform capabilities.
• Collaborate with development teams to provide reusable infrastructure components, golden paths, and platform patterns that accelerate delivery.
• Mentor principal and senior engineers, grow a community of practice, and raise engineering quality.
• May be called upon to support critical escalations and must be available during urgent IT incidents as needed.
• Design and build reusable data engineering frameworks and standards used across multiple squads to improve consistency, scalability, and delivery quality.
• Develop, manage, and optimize scalable pipelines that move data from SAP and legacy platforms into the enterprise data lake and deliver trusted data for reporting, analytics, AI, application and business consumption.
• Harmonize and transform data from disparate legacy systems into SAP while maintaining appropriate data quality, lineage, governance, and reconciliation controls.
• Provide hands-on technical leadership for Microsoft Data Ecosystem, Databricks and SAP Business Data Cloud solutions, including architecture decisions, implementation guidance, proof-of-concepts, and performance optimization.
• Drive the Reporting & Analytics engineering strategy and design API-driven and event-streaming solutions that enable secure, reliable, and timely access to enterprise data products.

• Bachelor's degree or equivalent years of work experience.
• 12+ years in data/platform engineering with enterprise scope and measurable impact.
• Mastery of data architecture (streaming and batch), lake/lakehouse/warehouse patterns, governance, and security.
• Proven leadership of automation, CI/CD for data, and observability at scale.
• Executive level communication, influence, and stakeholder alignment.
• Deep expertise with Databricks and modern lakehouse architecture, including Spark-based processing, Delta Lake, orchestration, governance, performance optimization, and production operations.
• Demonstrated experience designing, building, testing, and operating…