Manager / Senior Manager / Associate Director | Life Sciences Consulting
Enterprise Data Architecture • Cloud Modernization • Analytics & AI Enablement - Contractual role
Role purpose
We are looking for a senior AWS–Snowflake Data Architect to lead enterprise-scale data transformation—from architecture and platform modernization through implementation and adoption. The role requires equal strength in architecture judgement, delivery leadership and senior stakeholder engagement.
Experience in Life Sciences / Pharma / Biotech / MedTech is strongly preferred, particularly where data platforms support regulated, analytics-intensive or AI-enabled business processes.
What you will own
1. Enterprise data architecture
· Define current-state, target-state and transition architectures for enterprise data platforms.
· Architect modern data ecosystems on AWS and Snowflake across ingestion, storage, transformation, consumption and governance.
· Design fit-for-purpose data lake, lakehouse, warehouse and data-product patterns based on business requirements.
· Establish architecture principles, reference patterns, integration standards and reusable components.
· Make defensible trade-offs across performance, scalability, resilience, security, interoperability and cost.
2. AWS & Snowflake architecture
· Architect Snowflake environments across databases, schemas, warehouses, roles, resource monitors and workload patterns.
· Design AWS-native data solutions using relevant services such as S3, Glue, Lambda, Step Functions, DMS, Kinesis, IAM and CloudWatch.
· Define batch, streaming and near-real-time ingestion patterns for structured and semi-structured data.
· Design secure connectivity and data movement across cloud, SaaS, on-premise and external ecosystems.
· Drive Snowflake performance, workload and cost optimization; define scalability, resilience and disaster-recovery approaches.
3. Data engineering & integration
· Define architecture for ETL/ELT pipelines, APIs, event-driven integration and orchestration.
· Establish data modelling approaches across dimensional, normalized and domain/data-product patterns.
· Provide architectural oversight for data quality, metadata, lineage, master/reference data and observability.
· Guide engineering teams on design standards, reusable frameworks and implementation choices.
· Challenge designs that introduce unnecessary complexity, technical debt or cost.
4. Governance, security & compliance
· Embed security and governance into the architecture rather than treating them as downstream controls.
· Define patterns for RBAC, encryption, masking, tokenization, auditing, retention and access control.
· Enable lineage, traceability, data quality and controlled access across the data lifecycle.
· For Life Sciences environments, understand implications of GxP, 21 CFR Part 11, GDPR and applicable privacy requirements.
5. Analytics & AI readiness
· Design platforms that support enterprise reporting, advanced analytics, machine learning and GenAI use cases.
· Define governed mechanisms for making trusted enterprise data available to analytics and AI workloads.
· Partner with AI/ML, analytics and business teams to create reusable data foundations rather than isolated point solutions.
6. Architecture leadership & delivery
· Lead architecture workshops with business, data, security, infrastructure and application stakeholders.
· Convert ambiguous requirements into clear architecture decisions, implementation roadmaps and delivery dependencies.
· Own conceptual, logical and physical architecture artefacts, integration patterns and architecture decision records.
· Provide governance across design, build, testing, migration and deployment; identify architecture risks early and drive resolution.
· Provide technical leadership to architects, engineers and delivery teams.
Life Sciences experience | Preferred
Experience in one or more of the following domains is a strong advantage:
· Clinical Development / Clinical Operations; Clinical Data Management & Biostatistics
· Pharmacovigilance / Drug Safety; Regulatory Affairs; Medical Affairs
· Research & Discovery; Manufacturing / Quality
· Commercial / Patient data; Real-World Data / Real-World Evidence
Expectation: understand the business context behind the data—not simply its technical structure.
Core technical expectations
Must have
· Strong architecture experience with Snowflake and AWS, including enterprise-scale cloud data platforms.
· Strong understanding of Snowflake architecture, security, performance and cost optimization.
· Strong knowledge of AWS data and integration services.
· Experience with modern ETL/ELT, pipelines, orchestration, SQL and data modelling.
· Experience integrating cloud platforms with enterprise applications, SaaS platforms and/or on-premise systems.
· Strong grounding in data governance, security, metadata, lineage and data quality.
· Evidence of leading architecture through implementation—not architecture-on-paper alone.
Good to have
· Snowpark, Snowpipe, Streams & Tasks and Dynamic Tables.
· dbt and/or enterprise data integration platforms; Python.
· Terraform / Infrastructure as Code and CI/CD.
· Databricks or other modern data platforms; Kafka/Kinesis or event-driven architectures.
· Collibra, Alation or equivalent data cataloguing/governance platforms.
· AWS and/or Snowflake professional certifications.
Leadership expectations | Manager / Senior Manager
· Engage credibly with CIO, CTO, CDO, Data & Analytics and business leadership.
· Structure complex data problems and explain architecture choices in business language.
· Challenge requirements and technology choices where they do not create sufficient business value.
· Lead multidisciplinary architecture and engineering teams; mentor architects and engineers.
· Estimate delivery effort, dependencies and architecture implications; support proposals, solutioning, client workshops and technology assessments.
· Balance business value, engineering practicality, regulatory requirements, delivery risk and cost.
Looking for Immediate / join in 15 day's time line only.
关于高知特 (Cognizant)
高知特(Cognizant)（纳斯达克代码：CTSH）作为一家AI Builder和相关技术服务提供商，致力于通过打造全栈AI解决方案，帮助企业将人工智能投资转化为实际价值。公司凭借深厚的行业经验、流程优化和工程技术专长，将企业独特的业务场景融入科技系统，赋能组织释放人才潜能，推动切实成果，并帮助全球企业在瞬息万变的环境中保持领先。如需了解更多详情，敬请访问 cognizant.ai 或关注@cognizant。
补充雇佣信息
薪酬信息截至本职位发布之日为准。Cognizant 保留在适用法律允许的范围内随时修改该信息的权利。
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