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.