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

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.

À savoir avant de postuler

• Autorisation de travail: Cognizant ne prendra en considération que les candidats à ce poste qui sont légalement autorisés à travailler au Canada sans avoir besoin d'un parrainage de l'employeur, aujourd'hui ou à l'avenir.
• Mesures d’adaptation: Si vous avez un handicap qui nécessite des mesures d’adaptation raisonnable pour effectuer une recherche d’emploi ou poser une candidature, veuillez envoyer un courriel à [email protected] avec votre demande et vos coordonnées.
• L’IA dans notre processus de recrutement: Nous utilisons des outils d'intelligence artificielle (IA) pour trier et évaluer les candidatures efficacement. Notre équipe examine ensuite les candidatures et décide qui passe à l’étape suivante.
• Poste à pourvoir: Sauf indication contraire, ce poste est actuellement vacant et nous cherchons à le pourvoir.
• Exigences linguistiques: Nous demandons à tous les candidats de posséder une connaissance de base de l’anglais afin de faciliter les communications internes. Pour les postes au Québec, la capacité à communiquer efficacement en anglais est requise car vous fournirez des services à et collaborerez avec des parties prenantes anglophones situées hors de la province.
• Inclusion: Cognizant est un employeur souscrivant au principe de l’égalité d’accès à l’emploi. Votre candidature et votre dossier ne seront pas examinés en fonction de la race, de la couleur, du sexe, de la religion, des croyances, de l'orientation sexuelle, de l'identité de genre, de l'origine nationale, du handicap, des renseignements génétiques, de la grossesse, du statut d'ancien combattant ou de toute autre caractéristique protégée par les lois fédérales, provinciales ou locales.

À propos de Cognizant
Cognizant (NASDAQ: CTSH) est un AI Builder et une entreprise de services numériques (ESN) élaborant des solutions complètes d’IA maximisant les investissements pour des résultats concrets. Sa profonde expertise des métiers, des processus et des technologies lui permet d’intégrer dans les systèmes technologiques le contexte unique de chaque organisation de l’ingénierie à la production à l’échelle. Son objectif: améliorer l’efficacité des équipes, créer de la valeur et permettre aux grandes entreprises de rester performantes dans un monde qui évolue rapidement. Pour en savoir plus: cognizant.ai ou @cognizant.