Amazon Web Services Expert in Vienna

Technologies: Azure DevOps, Jira, Confluence, Office 365 (Teams, PowerPoint, Copilot, Excel, SharePoint, Forms, OneNote, PowerAutomate, Word), DataDog, Conceptboard, Python, AWS, Claude, Perplexity Methodology: Scrum

• Steering and supporting the organization in introducing Scrum and a SAFe-inspired quarterly roadmap and portfolio planning at program level.
• Servant leadership for Scrum teams and managers to achieve quarterly goals and measurably increase value creation for internal and external customers.
• Designing and building efficient communication and collaboration structures between multiple development teams and central stakeholders in the organization.
• Moderating and facilitating cross-functional workshops and management meetings to align on shared goals.
• Coaching leaders, Product Owners, and development teams on agile values, role understanding, and scaled ways of working (Scrum, SAFe, Business Agility).
• Removing structural obstacles at team and organizational level through targeted impediment management and escalation processes.
• Enabling teams to become more self-organized, accountable, and end-to-end responsible along the value streams.
• Individual coaching of key people (Product Owners, Chapter Leads, Scrum Masters) to strengthen their impact in the transformation program.
• Organization-wide identification, planning, and implementation of process, structure, and tool improvements to sustainably increase efficiency, throughput, and value creation.
• Close support for Product Owners in stakeholder management, business value prioritization, product strategy, roadmap planning, and effective collaboration with development teams.
• Introducing, governing, and scaling Azure DevOps as the central project and product management tool, including coaching the organization on usage, reporting, and alignment at all levels.

Achievements:

• Clear increase in visibility, transparency, and perceived value creation of the development teams within the overall organization.
• Noticeable improvement in communication between development, business, and other stakeholders.
• Optimization of work processes, workflow, and backlog structures, which reduced lead times, sharpened priorities, and sustainably increased team effectiveness.

• Designed and built a next-generation NLP platform to accelerate sales-driven customer service through intelligent request analysis and routing, reducing average customer query response time by 30%.
• Architected a hybrid NLP system combining Large Language Models (LLMs) with traditional NLP pipelines for robust, explainable results.
• Developed request classification and routing mechanisms to accelerate customer support teams in handling customer queries faster and more accurately.
• Optimized LLM based data extraction and classification with context engineering.
• Integrated the platform into customer service processes, reducing response times and enhancing workforce efficiency.

• Develop AWS-based components connecting Python backend services to React/TypeScript frontends.
• Improve database queries and application workflows for document processing and screening.
• Evaluate LLM retrieval and document-review workflows as part of the product stack.

Tech: AWS (Glue, Lambda, Redshift), Airflow, PostgreSQL, Python, PySpark, Metabase, Power BI

Delivered: Analytics-Ready Data Models • Legacy SQL to Cloud ETL Migration • Dynamic Pricing Engine

• Owned and evolved the company data warehouse end-to-end — from ingestion to transformation to analytics-ready dimensional data models on AWS Redshift.
• Collaborated with Analysts, Data Scientists, and business stakeholders to deliver scalable dimensional data models that enable self-serve analytics and streamline dashboarding in Metabase and Power BI.
• Architected end-to-end ETL/ELT pipelines on AWS (Glue, Lambda, Redshift) using Python and PySpark, orchestrated with Apache Airflow (MWAA) for reliability and observability.
• Defined and enforced data quality standards and governance practices across pipelines and the core data layer.
• Led migration of legacy SQL infrastructure into scalable AWS Glue pipelines with distributed PySpark processing, eliminating bottlenecks and reducing downtime.
• Developed a dynamic pricing engine applying automated promotional discounts based on occupancy rates, competitor pricing, and location performance tiers.
• Designed schema mappings to ingest MongoDB data into structured relational systems (Redshift/PostgreSQL).

Project Tech Stack: Python, AWS, Azure, FastAPI, openai, pandas, unittest/pymock

Achievements:

• Engineered automated data extraction pipelines to transform complex Excel datasets into structured formats via LLM-driven workflows.
• Architected a generative slide-deck engine that translates natural language prompts into formatted presentation assets.
• Integrated advanced LLM capabilities with the OpenAI Response API, implementing sophisticated tool-calling and structured output logic.
• Developed and containerized scalable backend microservice using FastAPI, Docker, and OpenShift to host and serve agentic skills.

• Foundational member of the backend TypeScript team, responsible for translating the initial product concept into a fully functional platform.
• Architected the core resource discovery engine for Azure and AWS, successfully enabling the frontend to visualize complex cloud environments and empowering the AI to evaluate architectural patterns.
• Engineered and scaled distributed APIs and dynamic CosmosDB schemas, ensuring the database and backend architecture could seamlessly adapt to rapidly evolving product requirements.
• Accelerated cross-functional testing by developing custom data-generation tools, allowing the AI and frontend teams to simulate and test against large-scale, artificial cloud architectures.
• Drove end-to-end feature delivery by partnering closely with AI, frontend, and business stakeholders to align technical implementation with the strategic vision of the Dawnguard platform.

• Shaping service management framework as part of head office of RBI
• Service owner overseeing IT cloud services, accountable for overall quality, lifecycle, and success
• Managing customer relationships and satisfaction for 10 business units ensuring smooth business continuity and operations
• Single point of contact and service/process owner providing T-shaped expertise in IT domains, cloud platforms and services (AWS, Azure, Kubernetes, Salesforce, ServiceNow, Jira, Orca, CRM, GitHub, SFMC)
• ITSM/ITIL implementation and mentoring including design, definition, mapping, and execution
• Change and release management, UAT/test assurance, major incident management, SDLC management
• Managing regulatory and audit requirements, DORA, GDPR, ITPF compliance and reporting
• Acting as a point of escalation for complex customer issues, resolving conflicts, fostering a customer-centric culture
• People management: leading and managing service delivery teams, DevOps, vendors, and third-party teams
• Cross-tribe service delivery and contract management: KPIs, SLAs, OLAs, RPO, RTO, BCM/SCM management
• Agile/Scrum process delivery governance, stakeholder communication, and budget/cost management

• Architected and deployed a highly available, multi-tenant Kubernetes platform on Google Kubernetes Engine (GKE) for business-critical microservices.
• Designed and implemented a comprehensive Infrastructure-as-Code strategy using Terraform for managing GKE clusters, networking (VPCs, Subnets), and IAM policies.
• Engineered optimized storage solutions for stateful workloads (MongoDB) using GKE PersistentVolumes, enhancing performance and data resiliency.
• Established a full-stack observability framework using Google Cloud Monitoring, Logging, Prometheus, and Grafana.

• Built Enterprise Architecture practice covering business, application, data, and integration layers
• Created a multi-year Integration Strategy, delivering annual cost savings of several hundred thousand euros
• Defined portfolio simplification roadmap, identifying redundant systems and cost reduction opportunities
• Designed target-state enterprise architecture around D365 FSCM, enabling a best-of-breed future landscape
• Developed Master Data Management (MDM) strategy and roadmap to improve data quality and operational efficiency
• Provided enterprise-wide architectural direction ensuring alignment and eliminating redundant initiatives
• Established reusable architecture patterns adopted by delivery teams
• Facilitated cross-functional architecture forums to align product and delivery teams
• Translated enterprise architecture guidance into practical delivery decisions
• Drove practical adoption of architecture guidance across initiatives rather than acting as a gatekeeper
• Identified redundant systems enabling substantial cost reduction potential
• Enabled cost transparency in integration landscape
• Enabled fact-based investment prioritization decisions

• Conducting a feasibility study on introducing an in-house DWH as a central data source
• Conducting workshops for current state analysis (processes, reports, KPIs) together with the clients
• Gathering and detailing business requirements including a target concept for an in-house DWH
• Coordinating and clarifying data deliveries and interfaces in meetings with stakeholders and data providers
• Developing initial data models as a basis for data quality and later implementation
• Drafting solution variants including architecture and operation options and decision basis
• Creating the business case including effort estimates, cost-benefit analysis, and decision report

• Architected enterprise AI strategy delivering 25% cost reduction through multi-LLM platform integration (Claude, OpenAI, Gemini) with intelligent routing and fallback optimization
• Drove operational excellence achieving 85% reduction in manual documentation and 3x faster campaign production through AI workflow automation and agent deployment across Slack and Microsoft Teams
• Established AI governance framework from ground up, implementing EU AI Act compliance, data redaction protocols, and safety filters aligned with DPIA standards
• Scaled AI adoption through structured onboarding programs, developing departmental workflows with secure access controls and comprehensive audit trails
• Implemented advanced RAG systems using Elasticsearch, Vertex AI, and Azure AI Foundry to improve knowledge access and output reliability
• Managed delivery through OKR framework coordinating cross-functional squads across 6-week project cycles to ensure strategic alignment and measurable results

• Migrated infrastructure automation from Puppet to Ansible
• Ensured compatibility with CentOS and RHEL 8 and 9
• Managed users in Active Directory
• Managed applications, services, security policies, certificates, and more

• I am responsible for providing solution architecture and technical assistance for a migration initiative aimed at transitioning to a new core banking system.
• The project involves replacing in-house development with a commercially available off-the-shelf core banking system operated as SaaS on AWS.
• Duties encompass solution design, technical coordination, supporting development teams, and providing IT support and troubleshooting.
• Managing over 10 global vendors and partners is crucial to project success.
• Technologies: VMWare ESXi, AWS, EKS Kubernetes, Mastercard Payment Transaction Service, Avaloq, A1 Telekom Austria infrastructure, CheckPoint FW, F5, Site-to-Site VPNs, PingID, Futurae SDK, Airlock, UC4/Automic, Firebase, iOS/Android lifecycle management, AWS IAM, AWS AppStream, AWS S3, AWS Transfer Family, QlikView, OpenAPI.

• Privacy-first, stateless e-invoice API providing AI-powered extraction, bidirectional format conversion (UBL/CII/ZUGFeRD), batch processing, and validation for German/Austrian e-invoice compliance
• Technologies: Nuxt 4, Vue 3, Nitro, TypeScript, Cloudflare (D1, R2), Stripe, OAuth, AI/LLM integration