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SAP/ERP Transformation - Lead Data Engineer

Ahold Delhaize USA

Salisbury, USA

Full-time

Senior · 12+ years experience

Hybrid

USD 160000-240000 yearly / year

Discovered 2 days ago

SAPTerraformAnsibleCloudFormationDataDogDatabricks

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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., DataDog).
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, CloudFormation) 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 scalable batch and real-time data pipelines using SQL and a modern programming language such as Python or Scala.
Strong knowledge of REST APIs, event-driven architectures, and streaming technologies such as Kafka or comparable platforms.
Strong background in reporting and analytics, including data modeling and delivery of trusted, analytics-ready datasets for enterprise consumption.
Proven ability to communicate complex technical concepts to technical and non-technical audiences, manage stakeholders, influence across teams, and serve as a bridge among engineering, business, and transformation teams.
Practical interest and experience in AI, Generative AI, and emerging technologies, supported by a demonstrated passion for continuous learning.
Experience productizing internal data platforms and measuring adoption/value.
Exposure to cost governance (FinOps) and multi cloud strategies.
Hands-on experience with SAP Business Data Cloud, Databricks, SAP Datasphere, or integration of SAP data products with an enterprise Databricks environment.
Experience supporting a large-scale SAP S/4HANA transformation and harmonizing data between legacy platforms, SAP, and enterprise analytical environments.
Databricks Data Engineer Professional certification or a comparable advanced cloud, data engineering, SAP, or architecture certification.
Experience with Delta Sharing, Unity Catalog, Lakeflow, Auto Loader, production-grade streaming workloads, and AI-ready or Generative AI data products.
Experience partnering with system integrators and shaping reporting and analytics strategy across multiple business domains or squads.