Enterprise Data & Analytics Architect

Key Responsibilities

• Own the end-to-end Data & Analytics architecture and technical vision for the enterprise.

• Define and drive the Data & Analytics strategy, roadmap, architecture principles, standards, and reference architectures.

• Design and architect enterprise-scale Data Lakehouse and Data Platform solutions.

• Lead architecture across Databricks, Snowflake, Cloudera, Azure, AWS, and GCP environments.

• Define architecture patterns for Delta Lake, Apache Iceberg, Hudi, Object Storage, Data Federation, and distributed data processing.

• Develop target-state architecture for enterprise data platforms, analytics platforms, Data Lakehouse, data warehouses, data products, and AI platforms.

• Define scalable and reusable architecture patterns supporting business intelligence, reporting, advanced analytics, AI/ML, and GenAI.

• Assess existing data and analytics capabilities and identify opportunities for modernization, optimization, simplification, and technology transformation.

• Ensure Data & Analytics architecture aligns with enterprise technology, security, regulatory, risk, and governance requirements.

• Architect solutions using Trino, Denodo, Dremio, Hive, Impala, and other distributed data technologies.

• Define and establish an enterprise data product strategy.

• Design reusable, discoverable, governed, and trusted data products for business and analytical consumption.

• Support development of data marketplaces and self-service data platforms.

• Enable data products to support BI, reporting, analytics, AI/ML, and operational use cases.

• Define architecture for Business Intelligence, reporting, dashboards, self-service analytics, and enterprise reporting platforms.

• Partner with business and analytics teams to understand KPIs, metrics, analytical requirements, and reporting needs.

• Support development of descriptive, diagnostic, predictive, and advanced analytics capabilities.

• Define scalable analytical data models and semantic layers for enterprise consumption.

• Enable trusted and consistent enterprise KPIs, metrics, and analytical insights.

• Promote self-service analytics while maintaining appropriate governance, security, and data quality.

• Architect solutions for AI/ML, Generative AI, RAG, Vector Databases, Graph Databases, embeddings, and agentic AI workloads.

• Design event-driven and streaming data architectures using Kafka, Flink, Spark Streaming, APIs, and messaging platforms.

• Define modern engineering practices for Data & Analytics platforms using Terraform, Kubernetes/OpenShift, Git, Jenkins, CI/CD, and DevOps tools.

• Partner with business leaders, product owners, data analysts, data scientists, engineering teams, technology architects, and vendors.

• Translate business requirements into practical and scalable Data & Analytics architecture.

• Review and approve solution designs, technical specifications, architecture decisions, and implementation approaches.

• Provide technical leadership to distributed engineering and delivery teams.

• Ensure projects follow enterprise architecture, security, governance, and engineering standards.

• Drive technical discussions, architecture reviews, design workshops, and technology evaluations.

Required Experience & Qualifications

• 10–15 years of experience in Data & Analytics, Data Architecture, Data Engineering, Analytics Architecture, or related disciplines.

• Strong experience designing and implementing enterprise-scale Data & Analytics platforms.

• Proven experience defining Data & Analytics strategy, architecture, roadmaps, and target-state architecture.

• Strong hands-on experience with Data Lakehouse, Data Warehouse, Data Platform, BI, and Advanced Analytics environments.

• Experience working in FSI/BFSI, banking, financial services, insurance, or other highly regulated environments is preferred.

• Strong expertise in one or more of Databricks, Snowflake, Cloudera, Azure, AWS, and GCP.

• Strong knowledge of Delta Lake, Apache Iceberg, Hudi, Object Storage, Data Federation, and distributed data platforms.

• Experience with Trino, Denodo, Dremio, Hive, Impala, or similar data technologies.

• Strong understanding of data modeling, data warehousing, dimensional modeling, semantic layers, metadata, lineage, and data governance.

• Experience designing data products, data marketplaces, analytical platforms, and self-service analytics.

• Strong understanding of BI, reporting, KPI management, analytical workloads, and advanced analytics.

• Experience with Kafka, Flink, Spark, Spark Streaming, APIs, real-time data, and event-driven architectures.

• Strong knowledge of RAG, Vector DB, Graph DB, embeddings, AI/ML, Generative AI, and agentic AI workloads.

• Experience with Terraform, Kubernetes/OpenShift, Git, Jenkins, CI/CD, and DevOps practices.

• Strong understanding of data security, privacy, access control, governance, data quality, and regulatory requirements.

• Experience with performance engineering, scalability, high availability, resiliency, and cloud cost optimization.