AWS - Data Architect - LS - CWR

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

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.