Senior Principal Engineer– Data

Job Purpose: We are seeking a highly skilled Senior Principal Engineer – Data Engineering, AI & Healthcare Analytics to architect and lead enterprise-scale data lake platforms at GSK. This is a hands-on technical architect role at the intersection of Azure cloud data engineering, metadata-driven intelligence, AI/ML frameworks, agentic AI systems, and healthcare domain expertise. You will design and own end-to-end ingestion, quality, and observability frameworks, infuse autonomous AI agents into data pipelines, and partner with clinical, commercial, and research stakeholders to deliver measurable value from GSK’s healthcare data assets. Key Responsibilities: Azure Data Engineering & Platform: • Lead the design and delivery of scalable Azure Services, Databricks, and enterprise Data Lakehouse solutions supporting patient, commercial, and healthcare data. • Design resilient batch, incremental, CDC, API, file-based, and real-time pipelines using ADF, Databricks, ADLS, Key Vault, Delta Lake, Apache Iceberg , Unity Catalog, Event Hubs, and Azure integration services. • Provide end-to-end technical leadership for bidirectional integrations between Salesforce PMP (Patient Management Services) and Data Lake, covering data extraction, transformation, synchronization, reconciliation, schema evolution, error handling, and downstream consumption. • Provide hands-on technical expertise across the Salesforce ecosystem, including Apex, SOQL, REST/Bulk APIs, authentication, integration patterns, and API development to enable secure and scalable Salesforce PMP integrations. • Define scalable Salesforce integration patterns using Salesforce APIs, Bulk API, platform events, incremental processing, and API throttling controls. • Drive Databricks performance engineering through Spark optimization, cluster and job sizing, Photon, caching, file compaction, data layout optimization, and workload monitoring. • Implement secure access patterns using Unity Catalog, Microsoft Entra ID groups, RBAC, managed identities, service principals, Key Vault, encryption, column masking, and row-level controls. • Drive Infrastructure-as-Code and automated deployments using Terraform/Bicep, Databricks Asset Bundles, GitHub Actions or Azure DevOps, and environment-specific configuration management. Enterprise Data Engineering Capabilities: • Design and drive metadata-driven enterprise capabilities for data ingestion, validation, monitoring, and automation. • Establish reusable, configuration-driven patterns that accelerate data onboarding and adoption across teams. • Leverage AI/ML and LLM capabilities to enhance metadata management, anomaly detection, mapping, and automated data controls. • Enable standardized data observability, quality monitoring, alerting, and operational controls. • Ensure capabilities are scalable, reusable, governed, auditable, and compliant with enterprise standards. • Drive adoption as shared enterprise capabilities, reducing duplicate engineering effort and improving consistency. Agentic AI Engineering & Integration: • Hands-on, day-to-day use of AI coding agents such as GitHub Copilot, Claude, Cursor, Devin, and Factory to accelerate engineering delivery. • Design and implement Agentic AI and RAG solutions using LangChain, LangGraph, LangSmith, MCP, and advanced Prompt Engineering techniques. • Build multi-agent orchestration workflows using LangGraph patterns such as supervisor-worker, specialized agents, tool calling, state management, and workflow coordination. • Design and integrate MCP servers to provide secure, governed access to enterprise tools, APIs, metadata, data platforms, and business services. • Develop RAG-based agent capabilities integrating enterprise data, metadata, vector stores, and governed knowledge s