The employer is a global biotechnology company focused on developing innovative therapies for serious diseases. It combines scientific research with advanced technology to deliver impactful treatments and improve patient outcomes worldwide.
Industry: Technology & Telecoms
Area: IT Data Analysis
Your responsibilities
Platform Strategy & Architecture
- Define and execute a multi-year vision and roadmap for enterprise data and AI platforms.
- Establish standards, frameworks, and best practices for data ingestion, integration, streaming, semantic models, analytics, and AI workloads.
- Design and evolve scalable platform architectures supporting multiple business domains and geographic regions.
- Lead architecture governance, technology assessments, and strategic platform investment decisions.
- Drive platform simplification initiatives by reducing tool fragmentation and technical debt.
- Manage strategic technology partnerships and vendor relationships.
AI & Advanced Analytics Enablement
- Leverage AI to improve metadata management, data quality, lineage, documentation, operational efficiency, and platform support.
- Define enterprise patterns for AI-enabled analytics, generative AI, conversational BI, retrieval-augmented generation (RAG), model observability, and responsible AI practices.
- Partner with business and technology stakeholders to scale successful AI initiatives into enterprise-wide capabilities.
- Maintain an innovation roadmap balancing business value, operational excellence, risk management, and regulatory requirements.
FinOps & Cost Optimization
- Own financial governance and cost efficiency across the data and AI platform landscape.
- Lead capacity planning, budget management, and cloud cost optimization initiatives.
- Establish cost transparency through chargeback/showback models, workload governance, and usage monitoring.
- Define measurable targets and KPIs for platform efficiency and value realization.
Engineering, Operations & Reliability
- Oversee platform engineering, cloud infrastructure, access management, automation, CI/CD, monitoring, and resilience.
- Deliver platform-as-a-product capabilities, including self-service provisioning, reusable frameworks, deployment templates, and developer enablement.
- Introduce and mature Site Reliability Engineering (SRE) practices covering availability, performance, incident response, capacity planning, and disaster recovery.
- Govern reusable accelerators, frameworks, and platform services as managed products with defined roadmaps and release plans.
- Ensure adherence to enterprise security, privacy, compliance, audit, and governance standards.
- Implement end-to-end observability for data products, pipelines, workloads, and operational health.
Leadership & Operating Model
- Build and lead a global team responsible for enterprise data and AI platform capabilities.
- Define effective engagement models between centralized platform organizations and distributed domain teams.
- Collaborate with senior business and technology leaders to align platform investments with strategic priorities.
- Foster communities of practice and drive adoption of platform standards and best practices.
- Develop technical talent through coaching, mentorship, architecture reviews, and capability-building initiatives.
What's on Offer
- Private medical care
- Annual bonus
- Employee stock purchase plan
- Flexible benefits budget
- Life insurance
- Hybrid work model (3 days in the office)
- Modern office in Warsaw
- 10+ years of experience in data platforms, data engineering, cloud data architecture, or a related domain.
- 5+ years of experience leading managers, senior engineers, architects, and global technical teams.
- Deep expertise in modern data platform architectures, including lakehouse and cloud-native data ecosystems.
- Hands-on experience with enterprise-scale cloud platforms, particularly Azure and/or AWS.
- Strong knowledge of Databricks or comparable large-scale analytics platforms.
- Experience delivering AI and GenAI platform capabilities, including MLOps, LLMOps, RAG, vector search, model serving, and AI-powered analytics.
- Strong background in Infrastructure as Code, automation, DevOps, and platform engineering.
- Proven success driving cloud FinOps, governance, cost optimization, and operational excellence initiatives.
- Familiarity with data governance, master data management, integration platforms, and enterprise data management practices.
- Experience operating within complex, highly regulated environments is highly desirable.
- Bachelor's degree in Computer Science, Engineering, Information Systems, or a related discipline; advanced degree preferred.
The employer is a global biotechnology company focused on developing innovative therapies for serious diseases. It combines scientific research with advanced technology to deliver impactful treatments and improve patient outcomes worldwide.
Industry: Technology & Telecoms
Area: IT Data Analysis
Your responsibilities
Platform Strategy & Architecture
- Define and execute a multi-year vision and roadmap for enterprise data and AI platforms.
- Establish standards, frameworks, and best practices for data ingestion, integration, streaming, semantic models, analytics, and AI workloads.
- Design and evolve scalable platform architectures supporting multiple business domains and geographic regions.
- Lead architecture governance, technology assessments, and strategic platform investment decisions.
- Drive platform simplification initiatives by reducing tool fragmentation and technical debt.
- Manage strategic technology partnerships and vendor relationships.
AI & Advanced Analytics Enablement
- Leverage AI to improve metadata management, data quality, lineage, documentation, operational efficiency, and platform support.
- Define enterprise patterns for AI-enabled analytics, generative AI, conversational BI, retrieval-augmented generation (RAG), model observability, and responsible AI practices.
- Partner with business and technology stakeholders to scale successful AI initiatives into enterprise-wide capabilities.
- Maintain an innovation roadmap balancing business value, operational excellence, risk management, and regulatory requirements.
FinOps & Cost Optimization
- Own financial governance and cost efficiency across the data and AI platform landscape.
- Lead capacity planning, budget management, and cloud cost optimization initiatives.
- Establish cost transparency through chargeback/showback models, workload governance, and usage monitoring.
- Define measurable targets and KPIs for platform efficiency and value realization.
Engineering, Operations & Reliability
- Oversee platform engineering, cloud infrastructure, access management, automation, CI/CD, monitoring, and resilience.
- Deliver platform-as-a-product capabilities, including self-service provisioning, reusable frameworks, deployment templates, and developer enablement.
- Introduce and mature Site Reliability Engineering (SRE) practices covering availability, performance, incident response, capacity planning, and disaster recovery.
- Govern reusable accelerators, frameworks, and platform services as managed products with defined roadmaps and release plans.
- Ensure adherence to enterprise security, privacy, compliance, audit, and governance standards.
- Implement end-to-end observability for data products, pipelines, workloads, and operational health.
Leadership & Operating Model
- Build and lead a global team responsible for enterprise data and AI platform capabilities.
- Define effective engagement models between centralized platform organizations and distributed domain teams.
- Collaborate with senior business and technology leaders to align platform investments with strategic priorities.
- Foster communities of practice and drive adoption of platform standards and best practices.
- Develop technical talent through coaching, mentorship, architecture reviews, and capability-building initiatives.
What's on Offer
- Private medical care
- Annual bonus
- Employee stock purchase plan
- Flexible benefits budget
- Life insurance
- Hybrid work model (3 days in the office)
- Modern office in Warsaw
Requirements: Cloud, Cloud platform, Azure, AWS, Databricks, Analytics platform, AI, MLOps, Infrastructure as Code, DevOps, Data management, Degree Additionally: Private healthcare.
