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.
What you’ll be doing:
- 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.
What we need to see:
- 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.
Ways to stand out from the crowd:
- 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.
NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative, driven to succeed, and enjoy learning while having fun, then what are you waiting for? Apply today!
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD.You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- 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
- Hands-on experience with Databricks, Apache Spark/PySpark, SQL, Delta Lake, and production-level data pipelines
- Experience modeling and building curated datasets from SAP S/4HANA or SAP ECC
- Knowledge of SAP business processes and data structures in finance, supply chain, procurement, sales, inventory, manufacturing, or master data
- Experience with Unity Catalog, including permissions, catalogs, schemas, tables, views, tags, comments, lineage, and data discovery
- Working knowledge of Immuta or similar governed data access platforms, including policy controls, masking, row-level security, user attributes, groups, and auditing
- Experience with data quality, reconciliation, testing, monitoring, and performance optimization for large-scale enterprise datasets
- Track record of supporting developers and improving engineering approaches, code quality, and production readiness
- Strong interpersonal and communication skills for technical and business collaboration
- Experience with SAP Business Data Cloud, SAP Datasphere, SAP BW, SAP SLT, ODP/ODQ, CDS views, BODS, or SAP APIs/OData
- Experience preparing SAP datasets for AI/ML, forecasting, anomaly detection, feature engineering, GenAI/RAG, and self-service analytics
- Familiarity with semantic modeling, metric views, certified datasets, business glossaries, and data-focused delivery
- Experience with Tableau, Power BI, Alteryx, Dataiku, or similar analytics and BI platforms
- Experience with CI/CD, Git, Databricks Asset Bundles, automated deployment, data privacy, least-privilege access, sensitive data classification, and enterprise data governance
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
-
Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
-
Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
-
Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”







