Enterprise Data & Analytics Architect

Key ResponsibilitiesOwn 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 & Qualifications10–15 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.