Availability awaiting confirmation
We are waiting for a fresh update from the source. This page preserves the last received job details; current availability is not confirmed.
Job description
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.
Overview
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 BusinessObjects 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.
Responsibilities
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., DataDog).
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 CloudFormation.
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.
Requirements
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 patterns.
Strong automation/scripting skills and version control practices.
Excellent cross team communication and influence.
Demonstrated experience leading complex SAP data conversion and migration workstreams across master and transactional data objects.
Advanced hands‑on expertise with SAP BusinessObjects Data Services (BODS), including job design, workflows, data stores, transformations, debugging, performance tuning, scheduling, and migration across environments.
Strong SQL, relational database, data mapping, profiling, reconciliation, ETL pipeline development, data quality, stewardship, governance, lineage, and audit‑control skills.
Proven ability to collaborate with analysts, SAP functional teams, engineers, business stakeholders, and external vendors to convert requirements into executable technical solutions.
Demonstrated experience designing, developing, testing, and operating distributed data pipelines using Apache Spark, SQL, and a modern programming language such as Python or Scala.
Experience with Databricks and modern cloud data engineering platforms, working with systems integrators , and governing vendor‑delivered technical artifacts.
Preferred Qualifications
Experience with feature stores and ML ready data patterns.
Knowledge of cost optimization and workload right sizing.
Experience supporting a large-scale SAP S/4HANA implementation, transformation, or multi‑wave migration program.
Deep hands‑on expertise with Databricks and modern lakehouse architecture, including Spark-based processing, Delta Lake, orchestration, governance, performance optimization, and production operations.
Salary Range NC: $163,280 - $244,920
#J-18808-Ljbffr
Worksite address
salisbury, NC, 28147, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.