What You Will Do:
- Define and maintain cloud modernization architecture standards, guiding principles, including network design, IAM/RBAC models, encryption, key management, logging, and audit controls.
- Architect scalable lakehouse data platforms with medallion architecture principles (raw → curated → consumable layers) optimized for analytics and AI workloads.
- Design ingestion and integration patterns supporting batch, streaming, and change data capture (CDC) across structured and semi-structured datasets.
- Establish enterprise-grade DataOps practices, including CI/CD for pipelines, infrastructure-as-code standards, automated testing, environment promotion strategies, and release governance.
- Define data quality frameworks, validation rules, reconciliation controls, and lineage standards to ensure trust and auditability.
- Optimize platform performance, cost management, scalability, and reliability across cloud environments.
- Define and enforce operational standards, including monitoring, alerting, runbooks, and reliability targets for data pipelines and services.
- Collaborate with Enterprise Solution Architect on cross-system integration patterns and non-functional requirement alignment.
- Partner with Functional Architect to ensure business definitions are accurately reflected in physical and logical data models.
- Mentor data engineers and provide architectural oversight across multiple delivery workstreams.
- Develop reusable platform accelerators, reference architectures, ingestion templates, and governance playbooks to increase delivery velocity and consistency.
What You Will Need:
- 8–11 years of experience in data engineering and cloud platform architecture.
- 3+ years designing and governing enterprise-grade cloud data platforms.
- Strong experience with Azure or AWS data ecosystems and lakehouse architectures (e.g., ADLS/S3, Databricks, Snowflake, or similar platforms).
- Experience designing ingestion frameworks using ELT/CDC patterns and distributed processing models.
- Expert Data Modelling skills in designing data marts, data products and semantic data models.
- Strong understanding of secure cloud architecture principles, including IAM, encryption, and auditability.
- Experience implementing CI/CD pipelines and infrastructure-as-code for data platforms.
- Experience defining operational metrics and monitoring strategies for production data platforms.
- Ability to lead architectural discussions and guide distributed engineering teams.
- Bachelor’s degree in computer science, engineering, or related discipline.
What Would Be Nice To Have:
- Experience supporting life sciences or other regulated industry data ecosystems.
- Familiarity with syndicated US pharma commercial datasets, CRM integrations, or specialty pharmacy/HUB data flows.
- Experience with real-time streaming architectures (e.g., Kafka or equivalent).
- Experience implementing data governance tools (catalog, lineage, data quality monitoring).
- Databricks or Snowflake cloud or data platform certifications.