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• Build, operate, and improve production data pipelines using Python, Airflow, Kubernetes, and related technologies
• Design scalable data pipelines and data models for growing data volumes and processing needs

• Configuration of the P&P platform end to end, across both frontend and backend
• Tuning and optimization of forecast and attribution models to match client data and business logic
• Writing custom business logic in SQL, Python, or Go where standard configuration isn’t enough

• Facilitation of discovery workshops with German-speaking clients to capture pricing and promotions requirements
• Documentation of business and technical specifications based on workshop outcomes
• Translation of business needs into functional configuration requirements for the solution team

• Owning quality architecture for the Scoring engine
• Prioritizing test strategies based on real-world risk
• Standardizing reusable patterns for the repository
• Building automated coverage across UI, API, and service layers
• Operating as a power user of AI-first IDEs

• Implementation and maintenance of large-scale data pipelines and the Azure/Databricks data platform
• Building robust, scalable pipelines using Azure Databricks (PySpark / SQL)
• Design and enforcement of data architecture standards including Delta Lake / Lakehouse architecture and data modeling

• Learning existing system architecture, codebase, tools, and development practices
• Validating existing technical documentation and flagging any gaps or outdated content
• Highlighting blockers to knowledge transfer
• Maintaining coding standards, version control practices, and agile routines

• Setup and maintenance of IT cloud infrastructure for multiple products
• Setup and maintenance of the automated build/deployment jobs
• Maintenance of various support tools and utilities
• Monitoring application performance and resource planning

• Writing production-quality, well-tested scraper code in Python
• Learning the architecture of the collection platform including scrapers, proxy layer, queues and enrichment API
• Debugging issues across multiple components using logging, monitoring and alerting tools

• Ownership of data engineering features, architecture, and code quality
• Design, implementation, and maintenance of Databricks-based data pipelines and workflows
• Building and optimization of ETL/ELT processes using Apache Spark on Databricks

• Odoo-modulok fejlesztése és testreszabása Python 3, XML, HTML használatával
• Backend-szolgáltatások fejlesztése Java/Spring és Hibernate segítségével
• Külső rendszerek összekapcsolása REST- és XML-RPC-API-k segítségével
• Eseményalapú integráció az Apache Kafka segítségével

• Design and own end-to-end solution architecture across distributed systems and cloud environments
• Translate business requirements into scalable technical solutions with clear articulation of trade-offs

• Design and propose changes to current data systems including migration paths and modernization strategies for legacy architectures
• Define and evolve the target architecture for data platforms covering ingestion, storage, processing, and consumption layers

• Architect and lead the development of large-scale ML systems and model pipelines
• Introduction of new ML systems such as traffic sign detection, building facade detection, and other computer vision-based solutions
• Definition and driving of the ML technical roadmap in alignment with company objectives

• End-to-end design for features across the assessment experience
• Research how educators and learners work with assessments
• Exploration of diverse directions for features on the unified platform
• Design for an institutional customer base with rigid adoption cycles

• Design and implement data models aligned with industry best practices
• Build and optimize scalable data warehousing and lakehouse solutions and ensure data integrity end-to-end
• Create and own robust ELT pipelines connecting multiple internal and external data sources

• Project management and execution of every stage of the project lifecycle
• Project planning and coordination including detailed project plans, timelines, delivery schedules, risk assessments, and mitigation strategies
• Resource management to meet project deliverables and optimize project profitability

• Frontend development for a greenfield project with API integration
• Contribution to proof-of-concept tasks
• Collaboration with Senior Developers
• Test-driven development for code quality
• Close work with Product Design, Product Management, and Backend teams

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• Building and maintaining SQL queries and reports that track bookings, pipeline coverage, and conversion
• Monitoring CRM data quality to resolve issues at the source
• Analyzing pipeline trends and supporting the weekly forecasting review cadence with clean, accurate data

• Shaping AWS-based cloud architectures and technical decisions for SaaS, PaaS, web application, and enterprise cloud environments
• Design and implementation of Infrastructure as Code solutions using tools such as Terraform, AWS CloudFormation, or Pulumi