Senior Databricks Developer, SAP S/4 Data Products and Governance

NVIDIA•Us, CA

About The Position

Join NVIDIA's team of world-class innovators and work with a dedicated group that drives the future of AI and Enterprise data products. As a Senior Databricks Developer, SAP S/4 Data Products and Governance, you will play a key role in enhancing our SAP S/4HANA data capabilities and advancing innovation in data products and governance. This is an outstanding chance to create a significant impact in a company known for its groundbreaking technology and inclusive culture.

Requirements

12+ years of experience in data engineering, analytics engineering, BI engineering, or enterprise data platform development.
Bachelor’s or Master’s degree (or equivalent experience) in Information Systems, Computer Science, or Business.
Solid hands-on experience with Databricks, Apache Spark/PySpark, SQL, Delta Lake, and constructing production-level data pipelines.
Proven experience modeling and building curated datasets from SAP S/4HANA or SAP ECC, with solid knowledge of SAP data structures and extraction methods.
Comprehensive knowledge of SAP business procedures and data structures in one or more domains such as finance, supply chain, procurement, sales, inventory, manufacturing, or master data.
Practical experience with Unity Catalog managing permissions, catalogs, schemas, tables, views, tags, comments, lineage, and data discovery within a regulated environment.
Working knowledge of Immuta or similar governed data access platforms, including policy‑based controls, masking, row‑level security, user attributes, groups, and auditing.
Experience applying data quality, reconciliation, testing, monitoring, and performance optimization for large‑scale, enterprise datasets.
Established track record of supporting other developers and advancing the team’s engineering approaches, code quality, and production readiness.
Strong interpersonal and communication skills for collaborating with both technical teams and business collaborators.

Nice To Haves

Practical experience working with SAP Business Data Cloud, SAP Datasphere, SAP BW, SAP SLT, ODP/ODQ, CDS views, BODS, or SAP APIs/OData for sophisticated data integration and extraction.
Track record of preparing SAP datasets for AI/ML, forecasting, anomaly detection, feature engineering, GenAI/RAG, and self‑service analytics and dashboards.
Familiarity with semantic modeling, metric views, certified datasets, business glossaries, and data-focused delivery.
Experience with modern analytics and BI tools such as Tableau, Power BI, Alteryx, Dataiku, or similar platforms.
Solid background in CI/CD, Git, Databricks Asset Bundles (or a comparable workflow orchestration), automated deployment, data privacy, least‑privilege access, sensitive data classification, and enterprise data governance.

Responsibilities

Compose, build, and maintain scalable Databricks pipelines using PySpark, SQL, Delta Lake, notebooks, and workflows, following reusable engineering patterns and performance guidelines.
Build SAP S/4HANA and ECC datasets across bronze, silver, and gold layers, encompassing finance, supply chain, procurement, order management, inventory, manufacturing, customer, supplier, and master data domains.
Convert SAP business processes into reliable facts, dimensions, metrics, measures, and reusable semantic data assets that aid analytics, reporting, AI/ML, and governed self-service.
Implement strong data quality, reconciliation, validation, lineage, observability, and production support measures to guarantee reliable, business‑ready datasets.
Work closely with SAP functional experts, business systems analysts, data architects, BI developers, and data scientists to understand source logic and deliver well‑documented, trusted data products.
Use Unity Catalog and Immuta to handle catalogs, schemas, tables, views, permissions, tags, comments, lineage, ownership, and specific access controls (row filters, masking, policy enforcement, auditing) consistent with governance standards.
Assess and apply SAP data extraction technologies (CDS views, ODP/ODQ, SLT, SAP Datasphere, SAP BW extractors, BODS, APIs/OData, replication flows, SAP Business Data Cloud) to build efficient, reliable ingestion patterns.
Advise data engineers and analysts, and produce reusable templates, patterns, and documentation to onboard new SAP domains and elevate the team’s overall engineering maturity.