Google Cloud Dataflow Experts in Germany

• Developed KPI dashboards using Power BI and DAX for 4+ business units, improving reporting transparency and strategic decision support.
• Migrated enterprise finance data views into dbt models, implementing modular SQL transformations, version-controlled data pipelines, and automated documentation to create a scalable analytics layer.
• Built dimensional data models in Snowflake for enterprise finance data, enabling scalable forecasting and supporting executive decision-making.
• Designed end-to-end ETL/ELT pipelines using Snowflake and SAP HANA, integrating data from 3+ enterprise systems.
• Automated monthly reporting workflows using SQL and Power BI, delivering strong business impact by reducing manual effort by 80%.
• Collaborated with finance stakeholders to translate business requirements into analytical data models, supporting strategic decision-making cycles.
• Delivered ad-hoc financial reports using Power BI, reducing turnaround time by 60%.
• Implemented data validation logic in SQL, resolving 95% of recurring data quality issues.

• Designed and implemented end-to-end data pipelines (batch & streaming) using Python, SQL, and Apache Spark, Databricks on AWS reducing ETL latency by 40%.
• Developed serverless event-driven ingestion pipelines using AWS Lambda and SQS, ensuring real-time data availability for downstream analytics.
• Leveraged Google Cloud Platform (GCP) services including BigQuery and Dataflow to manage cross-cloud data warehousing and analytics integration.
• Expertise in DMS (CDC, Full Load) and Airflow for scalable data pipeline automation and orchestration.
• Managed and customized data pipelines using Databricks, Airflow. Automation using Docker, Kubernetes, Terraform.
• Automated data quality checks using dbt to modularize transformations and ensure production-grade data lineage, improving reliability by 30%.
• Collaborated with compliance teams to ensure GDPR and SOC2 alignment. Mentored junior engineers and contributed to architecture refactoring for scalability.
• Created and maintained dashboards in Power BI to provide actionable insights.

• 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

• Architected a self-service Google Kubernetes Engine (GKE) platform for a major financial institution (Commerzbank), enabling 1000+ users across hundreds of product teams to autonomously provision resources and significantly accelerate development cycles.
• Designed a data-product-oriented platform architecture for the UK Department for Transport (DfT) to serve over 1500 direct end-users and numerous connected third-party systems, enhancing data accessibility and governance.
• Drove business growth by developing the strategic roadmap for the 'One Cloud' business line, targeting a 10% revenue increase.
• Served as a key member of the CTO Authority, providing strategic guidance on internal cloud initiatives and best practices.

• Set up and operated data-driven analysis and reporting processes in Power BI, Tableau, and SAP

• Integrated SAP FICO and Workday data into Power Platform workflows to automate HR reports

• Developed predictive ML models for workforce planning and KPI management

• Used Azure and GCP (BigQuery, Dataflow) to process large data volumes (Big Data pipelines)

• Automated reporting increased analysis efficiency by 40%

• Introduced a GCP-based analysis model for employee turnover

• Agile requirements management according to consultation guidelines
• Development of a master data system
• Preparatory activities for audits
• Managing the development team for the creation of microservices for billing preparation and execution
• Designing connections to various internal and external interfaces
• Designing release cycles and planning emergency management

• Lead Architect to design and implement the data lake and ETL Pipeline using AWS Stack
• Performance Optimization of Data Ingestion of ETL Pipeline
• Development of Data Validation using Great Expectations
• Leading of the data migration for two sources exchanges
• Data Modeling in AWS Redshift

MLOps

• Model inference implementation by mlflow and AWS SageMaker
• Feature Engineering for the running ML Models ( Recommender Engineer, Clustering )
• Implementatino of Model Registry and artifactory using mlflow
• Historization an Profiling of the Input Data Using AWS Glue Crawler and AWS Data Catalog
• Feature importance using mlflow

Tech. Stack: Python 3, AWS Glue, AWS Step Fucntion, AWS Lambda, AWS EventBridge, AWS IAM Role, AWS SageMaker, AWS EC2, AWS Glue Crawler, AWS CloudWatch, MLFlow, ETL, Data lake, GitHub Action, Terraform, Jenkins, Ansible playbooks (Infrastructure as Code), CI/CD, GitLab, SQL, PySparkSCRUM, Agile, Jira, BigData, VSCode, DBeaver, MSSQL, MySQL, grafana, Docker, Linux, Bash, MapReduce, Data Modeling (ORM), Pandas, YAML, SQL-Alchemy

• Led a team of 6 software engineers to develop and maintain an AI-driven healthcare platform, enabling automated diagnostics and prescriptions based on real-time ECG data analysis
• Designed and developed a robust Revenue Cycle Management (RCM) system, integrating HL7 and FHIR APIs to enable seamless interoperability, real-time data exchange, and HIPAA-compliant data handling, improving billing efficiency, claim processing, and regulatory adherence in healthcare operations
• Migrated a legacy monolithic application to a scalable microservices architecture, enhancing system modularity, scalability and maintainability while implementing key design patterns such as Strangler, Database-per-Service, API Gateway, Saga and CQRS for efficient service communication and transaction management
• Architected and led a C# 9/.NET 6 microservices ecosystem handling hotel reservations, payments, and loyalty programs, enabling 99.99% uptime across 10+ services
• Defined OpenAPI/Swagger contracts and auto-generated client SDKs, reducing front-to-backend integration time by 50%
• Containerized each service with Docker and orchestrated deployments via Kubernetes, slashing release lead time from days to hours
• Designed PostgreSQL schemas optimized for high-volume transactional workloads and implemented Redis caching layers to accelerate read-heavy endpoints by 80%
• Built Kafka streaming pipelines for real-time availability updates and audit logs, processing 2 million+ events per hour with end-to-end delivery guarantees
• Implemented unit and integration tests for React applications using Jest and React Testing Library, ensuring 80%+ test coverage, improving component reliability, and preventing regressions
• Defined and deployed AWS cloud infrastructure using Terraform, while containerizing and orchestrating microservices with Docker and Kubernetes, improving automation and system scalability
• Built a scalable full-stack booking application using React 18 and Django REST Framework, integrating Celery and Redis for asynchronous task processing, while deploying on GCP with Cloud Run and Firestore, enabling real-time scheduling, payment processing, and automated notifications
• Mentored junior developers through code reviews, pair programming, and knowledge-sharing sessions, improving team efficiency by 30% while maintaining comprehensive API documentation using Swagger/OpenAPI