Lead Consultant - Snowflake and Cortex AI Engineer

Job description

JOB TITLE: Lead Consultant - Snowflake and Cortex AI Engineer GCL: E Introduction to role Are you ready to harness Snowflake and Cortex AI to deliver meaningful outcomes for patients and the business? This role sits at the center of our data and AI transformation. It builds production-grade platforms and autonomous systems crafted to strengthen decision-making and accelerate impact across our US rare disease portfolio. You will serve as the technical anchor for a high-performing analytics and engineering team, setting standards and translating sophisticated data into secure, auditable, and scalable operational systems. From predictive patient identification and field force alerting to agent-driven insights and recommendations, your work will help power the next wave of data-driven commercial excellence. Join us to shape Snowflake-native architectures and autonomous AI solutions that move from idea to production with speed and rigor! Accountabilities Snowflake-Native ML Architecture: Define end-to-end ML platform architecture on Snowflake using Snowpark, Model Registry, Snowpark Containers, and Cortex AI as the primary compute and serving layer. Establish reusable patterns and frameworks that improve quality and speed delivery. Model Lifecycle and Serving: Create robust pipelines that move models from experimentation to production within Snowflake. Support patient identification, alignment prediction, next-best-action engines, and competitive intelligence through strong packaging, CI/CD, testing, versioning, and deployment practices using Cortex inference and Container Services. Advanced Data Pipeline Engineering: Engineer resilient batch and streaming workflows. Design feature stores, handle lineage, and enable feature reuse across initiatives. Production Operations and Optimization: Ensure reliability, performance, and cost efficiency. Implement logging, tracing, alerting, and health monitoring with Snowflake event tables and observability features. Monitor model performance, drift, and bias. Optimize warehouses, queries, and compute costs, with particular attention to the dynamics of small-population datasets. Cortex AI Agent Architecture: Design and deliver agentic AI systems. Break down sophisticated workflows into agent-executable steps while defining clear boundaries between autonomous execution and human oversight. Instruction Architecture and Prompt Engineering: Develop robust prompt architectures, agent skills and memories, semantic models, and context injection patterns optimized for Cortex AI. Translate analytics requirements into precise directives with clear acceptance criteria and guardrails. Autonomous Detection and Action: Build agents that identify anomalies such as competitive switching, discontinuation signals, or access changes. Enable them to validate hypotheses in Snowflake and route recommended actions to partners and CRM systems. Token Economics and Cost Control: Optimize cost and performance by handling context windows, token usage, chunking, caching strategies, and instruction design. Track consumption by workstream to balance capability with budget using Snowflake consumption tooling. Governance, Security, and Compliance: Apply version control, approvals, documentation, and audit trails using Snowflake governance capabilities such as object tagging, access history, classification, and lineage. Implement secrets management, RBAC, network policies, row access policies and dynamic masking. Technical Leadership and Enablement: Mentor engineers, lead design reviews, and define engineering standards. Provide Snowflake-native templates and guardrails that scale across teams. Partner with data scientists and commercial team to translate needs into effective solutions. Testing, Validation, and Release: Establish comprehensive testing for models and agent-generated code. Design validation pipelines with type checks, linting, integration and contract tests, and data quality assertions on Multifaceted Tables. Maintain clear architectural records, runbooks, and change processes to support smooth releases. Crucial skills/experience Education: Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field, or equivalent professional experience. Experience: 6–10+ years in Data Engineering, MLOps, or ML Platform roles, with a strong record of architecting and deploying ML solutions at scale. At least 3+ years building advanced data science or large-scale analytics solutions specifically on Snowflake. Snowflake Expertise: Deep expertise in Snowflake, including Snowpark (Python & Scala), Snowpark Container Services, Dynamic Tables, Streams, Tasks, Snowpipe, Snowflake Model Registry, UDFs/UDTFs/Stored Procedures, event tables, and performance optimization. Ability to architect Snowflake-native ML platforms without depending on external compute engines. Cortex AI Proficiency: Strong proficiency with Snowflake Cortex AI services, including Cortex LLM functions, Cortex Analyst, Cortex Search, Cortex Fine-Tuning, and embedding generation. Solid grasp of the process involved in developing and deploying production-grade AI applications fully within the Snowflake ecosystem. Programming: Proficiency in Python and SQL, with familiarity in TypeScript/JavaScript or a systems language such as Go or Rust. Deep experience with TDD, CI/CD pipelines, and code quality standards in a Snowflake-centric workflow. CI/CD & Infrastructure: Experience with CI/CD tools like GitHub Actions or Azure DevOps, containerization with Docker for Snowpark Containers, and Snowflake infrastructure-as-code approaches using Terraform, Schemachange, or SnowCLI. ML Tools: Practical knowledge of Snowflake Model Registry, experiment tracking, and model serving, supported by experience with external tools like MLflow or SageMaker for hybrid scenarios. AI/Agent Tools: Hands-on experience with AI coding tools such as Claude Code, GitHub Copilot, Cursor, or equivalent, alongside Cortex AI functioning as the main LLM serving platform. Working understanding of how LLMs reason about code and strong prompt engineering rigor. Security & Compliance: Solid understanding of healthcare data privacy and security within Snowflake, including RBAC, row access policies, dynamic data masking, network policies, secrets management, and audit controls. Systems Thinking & Architecture: Practical experience designing scalable interactions across traditional ML infrastructure and AI systems, with a preference for Snowflake-native solutions that minimize data movement and strengthen governance. Desirable skills/experience Knowledge of pharmaceutical commercial analytics in rare disease or specialty pharma, including HCP/HCO targeting, patient identification, call planning, demand forecasting, specialty pharmacy data, hub/PSP operations, and omnichannel measurement. Agent System Build: Experience building multi-agent workflows, orchestration patterns, and self governing systems for enterprise applications on Cortex AI or comparable platforms. Understanding of MCP (Model Context Protocol), tool-use patterns, and agent interoperability frameworks. Snowflake Advanced Features: Experience with Snowflake Data Sharing, Snowflake Marketplace, Iceberg Tables, hybrid tables, and cross-cloud replication for enterprise-scale data mesh architectures. Performance & Scalability: Experience with high-throughput inference on Snowpark Container Services, batch scoring at scale using Dynamic Tables, low-latency APIs via Snowflake external functions, and horizontal scalability for agent workloads. Enterprise Integration: Experience integrating Snowflake with Veeva, Salesforce, Microsoft 365, and ServiceNow APIs, using Snowflake external functions, connectors, and partner integrations to enable end-to-end automation. Snowflake Certifications: SnowPro Advanced Data Engineer, SnowPro Advanced Architect, or equivalent certifications. Communication & Collaboration: Excellent verbal and written communication skills, with the ability to communicate sophisticated architectural decisions and findings to both technical and non-technical audiences. Strong orientation toward mentorship and teamwork in a fast-paced, regulated environment. Why AstraZeneca When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world. Call to action If you’re ready to lead with Snowflake and Cortex AI, shape agentic systems that matter, and build architecture that delivers meaningful impact, we want to meet you! Date Posted 02-Sept-2026 Closing Date AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.