Key Responsibilities
AI Application Enablement: Support the development and deployment of AI-powered applications, including LLM/GenAI workflows, RAG pipelines, and integration with Mosaic AI tooling.
Data Platform Foundation: Design, build, and maintain the core Databricks Lakehouse platform, including workspace configuration, Unity Catalog setup, cluster policies, security, and governance.
Data Engineering: Develop and optimize production-grade data pipelines, delivering clean, reliable datasets for analytics and AI workloads.
Solution Architecture: Evaluate requirements, produce reference architectures, and recommend patterns for data ingestion, transformation, storage, and consumption that align with AI use cases.
Standards & Best Practices: Establish engineering standards for code quality, CI/CD (Databricks Asset Bundles, Repos), testing, monitoring, and cost optimization.
Collaboration: Work cross-functionally with data analysts, cloud/infra teams, and business units to translate requirements into scalable technical solutions.
Required Qualifications
5+ years in data engineering or platform engineering roles.
3+ years hands-on experience with Azure Databricks (workspace administration, Delta Lake, Spark, Unity Catalog).
Strong proficiency in Python and SQL.
Databricks workspace security: Proven experience implementing and managing workspace-level security, including network security (Private Link, VNet injection, IP access lists), identity and access management, cluster policies, secrets management, and RBAC at workspace, catalog, and object levels.
Experience building and operating production data pipelines at scale.
Experience with Mosaic AI (AI Playground, AI Functions, Agent Framework)
Familiarity with GenAI/LLM application patterns (RAG, prompt engineering, vector databases) is highly desirable.
Strong communication skills, ability to produce architecture diagrams, design documents, and present trade-offs to technical and non-technical audiences.
Nice to Have
Databricks certifications (Data Engineer Associate/Professional, Gen AI Engineer Associate).
Experience with Unity Catalog governance at enterprise scale (metastore design, data access policies, lineage, audit logging).
Hands-on experience with SAP integration , extracting data from S/4HANA, or BW/4HANA into a Lakehouse platform.
Experience leading or participating in data migration from SAP BW/4HANA to Databricks, including InfoProvider mapping, transformation logic re-implementation, data validation, and cutover planning.
Exposure to AI/ML workflows: model training infrastructure, feature stores, MLflow, or equivalent MLOps tooling — and ability to prepare SAP-sourced datasets for downstream ML consumption.
What You'll Be Part Of
A growing portfolio of AI initiatives ranging from predictive analytics to generative AI applications.
Migration of legacy to Databricks Lakehouse platform that will serve as the organization's single source of truth.
A collaborative, engineering-driven culture that values automation, quality, and continuous improvement.
Our most important asset is our People.
Vantiva’s success greatly relies on our people’s energy, motivation, and talent.
We are dedicated to cultivating a workforce that embraces and celebrates diversity as we believe our differences drive our creativity, and innovation.
We are proactive in supporting equality and maintaining an inclusive work environment, developing, and enhancing career opportunities for all.
Vantiva is committed to ensuring that all its employees receive a fair wage that is sufficient to live with dignity.
If you require a reasonable accommodation at any step of the application process, please let us know by answering the dedicated question in this application form.
