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.

Work arrangement
Hybrid