Apache Airflow Experts

Built an international e-commerce platform for a multi-vendor marketplace for digital assets from scratch. Designed and operated cloud native architectures at enterprise scale.

• Designed and operated a highly scalable microservice and serverless architecture
• Built the complete cloud infrastructure with Terraform + AWS CDK in AWS
• Provisioned ECS/EKS clusters (Fargate), Application Load Balancers (reverse proxy), and Lambda functions
• Observability & tracing with CloudWatch, DataDog, Prometheus, and Grafana
• End-to-end setup with DataDog (formerly AWS CloudWatch), Prometheus, and custom Grafana dashboards
• Integration of advanced metrics (including ORM mapper) and distributed tracing with Jaeger
• Robust backup and disaster recovery strategies
• RDS Postgres backups and hourly snapshots
• Read-only, asynchronously synchronized replicas with automated master failover in emergencies
• Minute-level rollback capability through versioned Docker images on ECS and Git-based CI/CD pipelines
• Created CI/CD pipelines with GitHub Actions for automated multi-stage deployments (Dev, Testing, Prod)
• Integrated Stripe for international payment processing
• Built a marketplace payment system with multiple parties and payout routines
• Used Algolia for high-performance real-time search of digital assets on the platform
• Federation of services with GraphQL and Hasura
• Later migration to GraphQL Mesh
• Test Driven Development (TDD) - unit, integration, and E2E testing with Jest, Vitest, and Playwright
• Used Next.js / React for modern frontend applications in the nx monorepo
• Enterprise security architecture & access control
• Integration of JWT tokens with Auth0, OAuth, OIDC, IP guards, BOLA protection, and secret vaults
• Authorization concepts with RBAC, ABAC, and native Postgres Row-Level Security (RLS)
• Built internal microfrontends with Retool for fast prototyping and operational business processes

Technologies: ABAC, AWS CDK, AWS CloudWatch, AWS ECS, AWS EKS, AWS Fargate, AWS RDS, AWS S3, Algolia, Auth0, DataDog, Docker, GitHub Actions, Grafana, GraphQL, GraphQL Mesh, Hasura, JWT, Jaeger, Java, JavaScript, Jest, Kotlin, Kubernetes, Monorepo, Next.js, OIDC, Playwright, Postgres, Postgres RLS, Prometheus, RBAC, Redis, Retool, Serverless, Stripe, Terraform, TypeScript, Vitest

• Fully on-premises LLM document-examination platform for a compliance-critical banking domain: agentic LangGraph pipeline with deterministic verification, every AI judgment structured and source-anchored; ~960 automated tests, zero data egress
• GPU throughput engineering (quantized serving, speculative decoding, prefix caching): 9.5x extraction speed-up, 500+ multi-document case files per day on a single A100
• AI-native EDI/EDIFACT integration platform (~116k LOC Java 25 / Spring Boot 4, 1,900+ tests): LLM-drafted partner mappings machine-verified before go-live (DFDL conformance, field-coverage checks, dry runs), ~99.5% byte match on real customer files — replacing weeks of manual mapping per partner

• This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.

Used: Python, RAG, LangGraph, LangChain, deepeval, MCP

• Task Engineering: Evaluating Coding Agents.
• Asset Creation: Building realistic task environments using datasets and files in German. Crucially, these assets must remain in the target language to genuinely measure multilingual handling.
• Prompting & Translation: finding failure points where AI does not work, in German.
• Implementation & Verification: Supporting the development of robust solutions (reference implementations) and write highly reliable, deterministic verifier scripts (using rubric-based judging only when strictly necessary).
• Calibration & Execution: Analyze execution logs and calibrate task difficulty (Easy to Very Hard) using standard Terminal-Bench run configurations against various model tiers (Haiku, Opus).
• Quality Assurance: Participation in a rigorous, 4-layer human quality control process (creation, human review, calibration review, and audit) alongside automated LLM-based checks to ensure fairness, grammatical accuracy, and benchmark integrity.
• Linguistic Review: Reviewing AI benchmark tasks across Hindi, Arabic, Japanese, Chinese, Czech and Turkish.

Scope: Embedded as FP&A Data & AI Architect within the Finance function of a major European refrigeration manufacturer, leading the transformation of manual, fragmented financial reporting into an automated, governance-driven intelligence platform. Driving the shift from Excel-based controlling to structured data architecture, Power BI analytics, and AI-assisted financial operations.

Financial Data Integrity & ERP Governance

• Initiated and led GL vs. subledger reconciliation investigations, identifying and resolving structural mismatches between General Ledger and subledger data that had gone undetected prior to engagement
• Conducted asset analysis to identify items missing from General Ledger postings, surfacing gaps in fixed asset tracking and period-end completeness
• Validated SAP reports, establishing baseline data quality standards for Finance team consumption
• Established systematic SAP data validation framework ensuring ongoing integrity between ERP postings and downstream reporting outputs

Finance Reporting Transformation

• Designed and implemented a structured Transformation Project approach for converting manual Finance reports into fully automated processes
• Created and owns the Data Reporting Audit Log; a centralized tracking system capturing report owners, stakeholders, data sources, manual effort estimates, and automation opportunity scores across the Finance function
• Mapped the full reporting landscape identifying quick-win automation targets and strategic Power BI migration candidates
• Actively reducing manual Excel and PowerPoint dependency across FP&A workflows; replacing point-in-time snapshots with live, governed data models

Power BI & Analytics Enablement

• Introduced and presented Power BI as the strategic reporting platform to Finance leadership, building internal buy-in for the BI transformation roadmap
• Designed initial Power BI architecture aligned with SAP, Salesforce and Oracle data structures and FP&A reporting requirements
• Established report ownership, governance documentation, and data lineage standards enabling sustainable self-service analytics across the Finance team

Transformation Infrastructure & Collaboration

• Configured and deployed Jira as the transformation project management hub, establishing structured sprint workflows, backlog management, and progress visibility for Finance IT initiatives
• Proposed and initiated a dedicated FP&A Communication & Transformation Hub, a structured cross-functional forum aligning Finance, IT, and business stakeholders around the reporting transformation roadmap
• Positioned the Finance function as an active driver of data governance and digital transformation within the broader organization

Outcomes

• GL/subledger reconciliation gaps identified and investigation framework established within first two weeks of engagement
• Data Reporting Audit Log deployed; first structured inventory of Finance reporting landscape in company history
• Power BI transformation roadmap presented and approved by Finance leadership
• Jira-based project governance live; Finance transformation now tracked with full sprint visibility

Technologies: SAP FI/CO · Power BI · DAX · SQL · Excel (advanced) · Power Query (M) · Power Automate · VBA · Jira · Microsoft 365 · SharePoint · Salesforce (Sales Data) · Oracle HCM · Python

• Architected and deployed private cloud data platform on VMware vSphere, integrating Greenplum MPP, Apache Kafka, Kubernetes, and Apache Solr, and developed real-time ingestion pipelines with Kafka Connect and Schema Registry.
• Led Oracle Exadata to Greenplum migration, rearchitected data models, optimized storage, implemented RabbitMQ with Debezium for CDC, and deployed VectorDB for Generative AI.
• Designed and executed multi-cloud migration PoC across AWS, Azure, and GCP, defined KPIs for throughput, latency, and cost efficiency, executed bulk data transfers, validated analytics and streaming workloads, and delivered full-scale architecture recommendations.
• Assessed legacy on-premises infrastructure and designed modern cloud-native data platforms using Greenplum and containerized microservices, advising on scalability, disaster recovery, and high-availability.

• Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
• Implementation of automated end-to-end cloud processes
• Development of LLM and NLP models
• Creation of interactive reports
• Support for national and international large corporations as well as medium-sized companies in implementing ML projects

Architected and maintained data products for renewable energy operations, covering wind turbine, grid-meter, and weather data. Built scalable ETL/ELT pipelines in Azure Databricks using Delta Lake (bronze/silver/gold layers) and processed data in various formats, including structured and semi-structured data. Contributed to a data quality framework supporting table and column documentation, outlier detection, and completeness metrics across all datasets within a data product. In addition, implemented a DORA KPI Databricks dashboard used across all data products. Optimized CI/CD processes in Azure DevOps to streamline deployment across development, test, and production environments.

Technology stack: Azure Databricks, PySpark, SQL, Delta Lake, Unity Catalog, Azure Data Lake, APIs, Dremio, Azure DevOps, YAML, Git, Databricks Workflows, Application Insights, Terraform, OpenAI API, Codex, LLM-assisted workflows

Led FP&A tech transformation for a 9-figure business – from resolving legacy technical debt to leading AI-native EPM implementation

• Driving end-to-end FP&A transformation, from architecture redesign through EPM tool selection to rollout
• Ran evaluation of 12+ EPM platforms, from vendor negotiation to selection framework tied to long-term planning
• Diagnosed constraints in financial planning architecture, presented findings to the CFO, and secured executive mandate to redesign FP&A infrastructure from the ground up

Independent engineering practice focused on custom AI systems, production delivery, and fractional technical leadership.

Selected client engagement: Scalutions

• Role: Serve as fractional CTO and hands-on technical lead, responsible for the architecture and agentic infrastructure behind its managed B2B outbound operation.

• Product: Designed and built OutboundLoop, an agentic SDR operating system for research, qualification, personalized outreach, campaign management, human approvals, measurement, and continuous improvement.

• Scope: Own the full system lifecycle—from business processes and agent behavior to context design, model routing, integrations, evaluation, telemetry, reliability, cost control, and production operations.

• Delivered MVP-focused full-stack architecture for a health-sector client: Vite/React frontend, backend services on Google Cloud Run, and Supabase for database plus IAM/authentication.
• Supported product requirements engineering and prioritized cost-aware workload placement, implementing browser-side/edge computation where feasible before moving logic to backend services.

• Design and build of a fully isolated AWS offline environment with no outbound internet access for running a browser-based business application
• Design and implementation of a proxy and response service that terminates all external application calls inside the VPC and serves them from locally stored content; identification of the actual communication needs through measurement-based DNS query logging
• Creation of architecture designs and decision papers including a comparison of options (Application Load Balancer with Lambda and S3, reverse proxy on EC2, private API Gateway) assessed by operational effort, cost, and availability
• Transfer of the solution and operations documentation previously available only for Azure to an AWS target architecture, including reassignment of all services and operational processes
• Automated rollout as Infrastructure as Code (Terraform, CloudFormation) with CI deployment via GitHub Actions, plus setup of private DNS zones and an internal certificate chain for operation without internet access
• Creation of architecture, deployment, and operations documentation and handover to the customer
• Build-up of a private cloud platform on OpenStack at provider TelemaxX with Terraform, including FortiGate HA clusters, FortiManager, and Kubernetes
• Introduction of Policy as Code (Open Policy Agent, Conftest) as well as development of MCP servers (Model Context Protocol) to connect AI assistants to operations and project tools

Successes:

• Made the business application fully operable without internet access for the first time; the cause of the loading error was narrowed down systematically to missing CORS headers after the likely certificate issue was ruled out
• Fully transferred an existing Azure concept to AWS and replaced the manually created environment with a reproducible, CI-based rollout

Technology stack: AWS (VPC, Application Load Balancer, Lambda, S3, Route 53 private hosted zones and Resolver query logging, IAM, CloudWatch, EC2, CloudFormation), Infrastructure as Code (Terraform, CloudFormation, Remote State), CI/CD (GitHub Actions with OIDC, Azure DevOps Pipelines), OpenStack, FortiGate, FortiManager, Kubernetes, Policy as Code (Open Policy Agent, Conftest), offline and air-gap architectures, PKI & certificates (internal CA, TLS, CRL/OCSP), DNS, network segmentation, Linux, Windows Server, Python, Bash, PowerShell, YAML, JSON, architecture design & decision papers, documentation (Confluence, Markdown), Generative & Agentic AI (Model Context Protocol, Agentic AI Coding Tools)

I designed and developed an AI-powered multi-tenant platform on Azure that transforms SAP process recordings into technical documentation, presentations and automated tests, processing over 15,000 process recordings for enterprise customers like Würth. I owned the architecture, the production releases and the DevOps setup. I also designed a multi-tenant system with SSO and role-based access on Azure. Implemented an MCP Server with Dynamic OAuth Authentication.

Main Tasks:

• Sprint planning and feature preparation
• Design the multi-tenant platform architecture (FastAPI, SQLAlchemy, PostgreSQL row-level security for tenant isolation)
• Develop AI pipelines with Prefect for transcription (Azure Speech API), document generation and SAP screen-recording analysis (Claude, gpt-4-mini)
• Design and implement an MCP server to expose tenant knowledge to LLM clients (Claude), with async retrieval and reranking
• Implement LLM cost tracking, rate limiting and client pooling for Anthropic/OpenAI/Azure OpenAI endpoints
• Set up CI/CD: Docker images to Azure Container Registry, GitHub Actions, Azure Static Web Apps, Alembic migrations in containers
• Manage production releases and execute live data migrations for enterprise customers
• Define engineering standards and architecture patterns for the team

Environment: Azure / Azure Foundry / Python / FastAPI / Prefect / React / PostgreSQL

• Mentor team of 6 ML platform engineers through weekly 1:1s, technical design reviews, and best practices, improving team velocity by 35% through structured sprint planning and skill development programs
• Define 2025–2026 ML platform roadmap in collaboration with Data Science, Cloud Engineering, and Product teams, prioritizing automated model governance, cost attribution systems, and multi-environment deployment strategies
• Partner with Data Science, SRE, and Product stakeholders to align ML platform capabilities with business objectives, reducing data scientist deployment friction by 60% through self-service platforms
• Architect and deliver production-grade MLOps platform supporting 50+ models in production with automated promotion pipelines, versioning, and rollback capabilities, achieving 99.5% platform uptime SLA
• Design distributed ML pipeline architecture using Metaflow and Argo Workflows (Vertex Pipelines-compatible), reducing model training time by 30% and deployment cycles from 2 weeks to 3 days through full CI/CD automation
• Build containerized ML services on Kubernetes with auto-scaling policies, resource quotas, and multi-tenancy isolation, optimizing infrastructure costs by $180K annually (25% reduction)
• Implement monitoring, alerting, and performance tracking using Prometheus, Grafana, and custom instrumentation, reducing model debugging time by 50% and establishing model performance SLOs
• Lead development of RAG-based document intelligence platform using LangChain, LangGraph, and vector databases, implementing agentic AI workflows for automated financial document processing
• Implement Infrastructure-as-Code using Terraform for reproducible environment provisioning and GitOps workflows, reducing infrastructure drift incidents by 80%
• Design role-based access control for ML platform, implement model lineage tracking, and establish audit trails for regulatory compliance aligned with enterprise IAM best practices