Databricks Data & Ai Analytics Architect

Job Summary

Role Description:

We are seeking an experienced Data & AI Analytics Architect / Lead Engineer to modernize enterprise analytics platforms and drive adoption of AI-powered analytical solutions on Databricks. The role focuses on building scalable data ingestion frameworks metadata-driven architectures conversational analytic capabilities and governed data products that improve business decision-making and reduce time to insight.

The organization operates multiple reporting and analytics solutions across Trading Risk Finance and Operations using a mix of legacy data warehouse technologies and custom data pipelines.

Current challenges include:

Fragmented ingestion and transformation patterns resulting in higher maintenance effort.

Ongoing migration of analytical workloads from legacy platforms to Databricks.

Increasing demand for self-service analytics conversational data access and AI-assisted decision support.

Need for improved data quality lineage governance and auditability.

Requirement to accelerate insight generation and improve analytical scalability reliability and performance.

Skills:

Strong expertise in Databricks Spark Delta Lake Unity Catalog SQL Warehouses Databricks Workflows Databricks Genie and Genie One.

Experience designing enterprise-scale data ingestion ETL/ELT metadata-driven and watermark-based processing frameworks.

Experience modernizing and migrating workloads from legacy data warehouses to Databricks.

Strong SQL skills including query optimization semantic modeling and metadata-driven analytics.

Experience building AI/LLM-powered analytics solutions conversational interfaces and agent-based architectures.

Experience developing analytical assistants/copilots leveraging business metadata governance rules and semantic context.

Ability to design conversational data exploration with drill-down analysis context retention and root-cause investigation.

Experience with automated insight generation including KPI monitoring trend analysis anomaly detection correlation analysis and executive summaries.

Good understanding of cloud data and analytics architectures covering ingestion transformation modeling and serving layers.

Knowledge of visualization and dashboarding concepts enabling business-friendly analytical experiences.

Ability to work effectively across business and technology teams to improve data availability reliability and performance.

Familiarity with Trading Risk Finance Operations or Supply Chain domains is highly desirable.

Remote Work :

No

Employment Type :

Full-time

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