Key Responsibilities :
Solution Architecture & Design :
- Define best practices for data pipelines, orchestration, and governance using Lakehouse architecture.
- Partner with product, business, and engineering teams to align architecture with business outcomes.
Databricks & AI Expertise :
- Hands-on implementation and optimization of Databricks AI tools, including :- Agent Bricks (AI agent orchestration)
- Lakeflow Designer (data workflow automation)
- MLflow (ML lifecycle management)
- Genie or Mosaic AI (generative AI & enterprise search)
- At least one end-to-end implementation experience of AI/ML workloads using Databricks.
- Architect large-scale data platforms leveraging Databricks for advanced analytics, AI/ML, and real-time data processing.
- Stay updated with the latest industry trends in AI, MLOps, LLMOps, and data architectures.
Leadership & Stakeholder Engagement :
- Lead technical discussions and workshops with cross-functional stakeholders across global geographies.
- Provide technical thought leadership and mentor junior engineers/architects.
- Collaborate with enterprise architects, data scientists, and cloud engineers to ensure architectural consistency.
Innovation & Best Practices :
- Establish architectural blueprints, standards, and data governance practices.
- Evaluate new tools, frameworks, and methodologies for adoption in the data ecosystem.
- Drive performance tuning, cost optimization, and scalability in Databricks and AI solutions.
Must-Have Skills :
- 10+ years in data engineering/architecture, with strong hands-on experience in Databricks.
- Expertise in Databricks AI suite (Agent Bricks, Lakeflow Designer, MLflow, Genie or Mosaic AI).
- Proven end-to-end AI/ML implementation experience on Databricks.
- Strong knowledge of Delta Lake, PySpark, SQL, and Data Lakehouse design patterns.
- Excellent understanding of MLOps / LLMOps, model deployment, and monitoring.
- Strong communication and stakeholder management skills with global exposure.
Good-to-Have Skills :
- Cloud platform experience (Azure, AWS, or GCP) with Databricks.
- Exposure to GenAI, Vector Databases, RAG frameworks.
- Familiarity with data security, compliance, and privacy regulations.
- Industry certifications (e.g., Databricks Certified Data Architect / ML Professional).