Lead Data & Integration Engineer (Data & GenAI)
Key Responsibilities
System Analysis & Solution Design
Analyze business and technical requirements and translate them into end-to-end system integration and data flow designs. Gather, document, and validate functional and non-functional requirements with business and technical stakeholders. Define data contracts, interface specifications, and integration requirements across upstream and downstream systems. Perform impact analysis, gap analysis, and root cause analysis for system enhancements and integrations. Identify process inefficiencies, risks, and bottlenecks, and recommend scalable, maintainable solutions. Prepare Business Requirements Documents (BRD), Functional Specifications (FRD), interface specifications, and process flow diagrams. Integration & Data Movement
Design and coordinate data movement across enterprise systems using APIs, SFTP, file-based integrations, and batch processing. Support integrations within enterprise Data Lake environments, including Informatica, Cloudera, and related data platforms. Define data mapping, transformation, validation, and reconciliation requirements. Collaborate with development teams to implement reliable and secure system integrations. Troubleshoot integration issues and coordinate resolution across development, testing, and production environments. Ensure integration reliability through monitoring, logging, exception handling, and error management. Data Preparation for GenAI
Support data ingestion and preparation for Generative AI and enterprise AI initiatives. Design processes for document ingestion, data aggregation, cleansing, transformation, and enrichment. Work with structured, semi-structured, and unstructured data sources. Prepare high-quality data for Retrieval-Augmented Generation (RAG), enterprise search, AI assistants, and investigation workflows. Collaborate with AI, data engineering, and platform teams to ensure data readiness for downstream AI applications. Ensure data governance, consistency, and quality across AI-enabled solutions. Delivery & Stakeholder Management
Collaborate with cross-functional teams including Data Engineering, Infrastructure, Security, Application Development, and Business stakeholders. Coordinate System Integration Testing (SIT), User Acceptance Testing (UAT), production deployment, and post-implementation support. Manage project risks, issues, dependencies, and change requests throughout the SDLC. Facilitate stakeholder workshops, requirement walkthroughs, and solution review sessions. Maintain comprehensive documentation for interfaces, data mappings, workflows, and operational procedures. Support Agile ceremonies including sprint planning, backlog refinement, daily stand-ups, and retrospectives.
System Analysis & Solution Design
Analyze business and technical requirements and translate them into end-to-end system integration and data flow designs. Gather, document, and validate functional and non-functional requirements with business and technical stakeholders. Define data contracts, interface specifications, and integration requirements across upstream and downstream systems. Perform impact analysis, gap analysis, and root cause analysis for system enhancements and integrations. Identify process inefficiencies, risks, and bottlenecks, and recommend scalable, maintainable solutions. Prepare Business Requirements Documents (BRD), Functional Specifications (FRD), interface specifications, and process flow diagrams. Integration & Data Movement
Design and coordinate data movement across enterprise systems using APIs, SFTP, file-based integrations, and batch processing. Support integrations within enterprise Data Lake environments, including Informatica, Cloudera, and related data platforms. Define data mapping, transformation, validation, and reconciliation requirements. Collaborate with development teams to implement reliable and secure system integrations. Troubleshoot integration issues and coordinate resolution across development, testing, and production environments. Ensure integration reliability through monitoring, logging, exception handling, and error management. Data Preparation for GenAI
Support data ingestion and preparation for Generative AI and enterprise AI initiatives. Design processes for document ingestion, data aggregation, cleansing, transformation, and enrichment. Work with structured, semi-structured, and unstructured data sources. Prepare high-quality data for Retrieval-Augmented Generation (RAG), enterprise search, AI assistants, and investigation workflows. Collaborate with AI, data engineering, and platform teams to ensure data readiness for downstream AI applications. Ensure data governance, consistency, and quality across AI-enabled solutions. Delivery & Stakeholder Management
Collaborate with cross-functional teams including Data Engineering, Infrastructure, Security, Application Development, and Business stakeholders. Coordinate System Integration Testing (SIT), User Acceptance Testing (UAT), production deployment, and post-implementation support. Manage project risks, issues, dependencies, and change requests throughout the SDLC. Facilitate stakeholder workshops, requirement walkthroughs, and solution review sessions. Maintain comprehensive documentation for interfaces, data mappings, workflows, and operational procedures. Support Agile ceremonies including sprint planning, backlog refinement, daily stand-ups, and retrospectives.