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 sources.
* Engineer agents as containerized, API-enabled microservices, integrating them with enterprise cloud platforms and existing data engineering workflows.
* Implement agent observability, tracing, evaluation, testing, and monitoring using LangSmith and related enterprise observability capabilities.
* Establish technical controls for agent security, authentication, authorization, human-in-the-loop, auditability, error handling, and responsible AI.

Healthcare Domain Data Expertise:

* Strong understanding of patient and Patient Management Services data, including patient journeys, enrollment, access, services, and interactions.
* Knowledge of related clinical, commercial, market access, and real-world healthcare data domains.
* Experience integrating and governing patient data across Salesforce, Data Lake/Lakehouse, analytics, and downstream platforms.
* Ensure patient data solutions meet data quality, security, privacy, lineage, GxP, HIPAA, GDPR, and enterprise governance requirements.

Innovation & Continuous Improvement:

* Drive technical innovation across the Data Lake and Salesforce PMP ecosystem using GenAI, Agentic AI, Conversational AI, and intelligent automation.
* Develop AI agents for metadata discovery, impact analysis, schema mapping, data quality diagnostics, pipeline monitoring, failure remediation, and operational support.
* Evaluate and integrate emerging capabilities such as MCP, LangGraph, LangChain, RAG, vector databases, Salesforce Agentforce, and Databricks AI services.
* Identify opportunities to modernize bidirectional Salesforce PMP and Data Lake integrations through event-driven processing, CDC, automated reconciliation, API orchestration, and near-real-time data synchronization.
* Build reusable technical accelerators for Salesforce object onboarding, Apex/API integration, schema-change detection, automated testing, and deployment automation.
* Convert successful POCs into secure, governed, observable, and production-ready enterprise capabilities that can be reused across patient and commercial data teams.

Forward Deployed Engineering (FDE) & Solution Enablement:

* Partner directly with Patient Management Services, Salesforce, Data Lake, product, and engineering teams to translate complex patient-services requirements into production-ready technical solutions.
* Lead rapid discovery and prototyping across Salesforce Apex, REST/Bulk APIs, SOQL, platform events, ADF, Databricks, Delta Lake, Unity Catalog, and downstream data products.
* Design and validate end-to-end Salesforce PMP-to-Data Lake and Data Lake-to-PMP data flows, including authentication, orchestration, transformation, synchronization, and downstream consumption.
* Resolve complex integration challenges involving API limits, high-volume processing, schema evolution, duplicate records, retries, idempotency, reconciliation, error handling, and data recovery.
* Build technical prototypes and reference implementations for GenAI, RAG, multi-agent orchestration, MCP-based tool access, and AI-enabled patient-services use cases.
* Embed production controls covering security, consent and privacy, PHI/PII protection, data quality, observability, auditability, performance, and operational readiness.
* Perform end-to-end impact analysis for Salesforce PMP changes across objects, fields, Apex services, APIs, Data Lake pipelines, data models, extracts, reports, and downstream consumers.

Required skills:

* 10+ years of data engineering/platform experience, including significant technical leadership at Senior/Principal Engineer level.
* Deep hands-on expertise across Azure, Databricks and enterprise Data Lakehouse platforms, including Spark/PySpark, Delta Lake, Apache Iceberg , streaming, Unity Catalog, ADF, ADLS, Key Vault, and Azure integration services.
* Proven experience designing enterprise-scale, metadata-driven ingestion and engineering capabilities, including batch, incremental, CDC, API, real-time processing, data quality, observability, and automated controls.
* Strong hands-on knowledge of the Salesforce ecosystem, including Apex, SOQL, REST/Bulk APIs, authentication, integration patterns, and bidirectional Salesforce-to-Data Lake integrations.

View and apply on HireChats