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.

Valuable 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.

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