Primary Purpose: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 Master Data Lead, accountable for the engineering approach to Master Data, S/4 and integrating master data into SAP S/4. Partners with master data engineering teams, product teams, data analysts, business owners, and implementation vendors to deliver a end to end deign for master data across product, vendor and location.
*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.Duties and 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 the end-to-end master data strategy, design, and delivery across product, vendor, and location domains, with Stibo Systems MDM as the system of governance and SAP S/4HANA master data setup aligned to business and transformation requirements.
- Define and oversee Stibo MDM data models, hierarchies, workflows, business rules, match-and-merge logic, reference data, approvals, and integrations required to manage product, vendor, and location master data at enterprise scale.
- Partner with SAP functional teams, data stewards, business owners, and source-system teams to translate master data requirements into canonical models, field mappings, validation rules, ownership standards, and executable specifications for Stibo and SAP.
- Direct technical delivery across internal engineering teams, Stibo specialists, SAP implementation partners, and systems integrators; review solution designs and configurations, manage dependencies and defects, and ensure adherence to enterprise architecture and engineering standards.
- Establish automated data-quality controls, stewardship workflows, lineage, auditability, reconciliation, monitoring, and exception management across Stibo and SAP to deliver complete, accurate, governed, and traceable master data.
- Plan and lead master data testing, mock loads, migration rehearsals, cutover, SAP master data initialization, post-load validation, and production stabilization, including rollback, reprocessing, and root-cause remediation across legacy systems, Stibo, and SAP.
Qualifications
- 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 leadership of enterprise master data programs spanning product, vendor, and location domains, including Stibo MDM implementation, SAP S/4HANA master data setup, data conversion, migration, cutover, and production stabilization.
- Deep functional and technical expertise in Stibo Systems MDM, including multidomain data modeling, hierarchies, workflows, business rules, reference data, match-and-merge, approvals, integrations, and operational support.
- Strong command of master data architecture and governance, including canonical models, data standards, stewardship, ownership, quality rules, profiling, lineage, reconciliation, audit controls, privacy, and lifecycle management.
- Proven ability to influence business owners, data stewards, product teams, SAP functional leads, architects, engineers, and implementation partners; translate business requirements into executable MDM designs and drive decisions across complex dependencies.
- Strong data engineering skills across SQL, Python or Scala, APIs, event and batch integration, and Apache Spark, with the ability to design scalable pipelines that connect source systems, Stibo, SAP, analytics platforms, and downstream consumers.
- Experience with Databricks and modern cloud or lakehouse platforms for master data processing, quality validation, observability, analytics, and AI/ML enablement, including developing governed, trusted, and reusable master data products that support AI-ready enterprise use cases.
Preferred Qualifications- Experience with feature stores and ML ready data patterns.
- Knowledge of cost optimization and workload right sizing.
- 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
Actual compensation offered to a candidate may vary based on their unique qualifications and experience, internal equity, and market conditions. Final compensation decisions will be made in accordance with company policies and applicable laws.
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