Requirements engineer, Python Developer

Consolidation and harmonization of master address data. Goal: Project management in the area of consolidation and harmonization of master address data.

• Designed and validated logical data models to consolidate and streamline master data management.
• Applied SQL for comprehensive analysis and to ensure data consistency during consolidation.
• Facilitated workshops with stakeholders to align on the AS-IS-Analysis and drive business requirements.
• Created detailed documentation to support cross-functional communication and decision-making.
• Developed risk analysis and decision templates, ensuring business continuity and reducing potential data inconsistencies.

Technologies: Requirements Engineering, Eraser.io, Notion.so, UML, Miro.

Requirements- and Data Engineering and for BI Pipeline. Goal: Automate the cash flow analysis to have an automated daily overview instead of a monthly manual insight.

• Captured comprehensive stakeholder requirements for visualized quality assurance through detailed interviews and workshops.
• Reviewed requirements for consistency, conflicts, and feasibility, ensuring alignment with project goals and deliverables.
• Designed and tested standard business processes, developing process maps and implementing robust testing protocols.
• Created and prioritized user stories with clear acceptance criteria, maintaining a well-groomed product backlog.
• Planned and executed device management functionalities, coordinating with development teams to ensure timely delivery.
• Produced detailed system and process documentation, including training materials and user guides for end-user proficiency.
• Managed comprehensive test strategies, utilizing automated tools for planning, execution, and documentation of tests.
• Conducted systematic evaluations of functionalities against acceptance criteria, ensuring compliance and optimal performance.
• Reviewed features and user stories for consistency, overlaps, and synergies, optimizing resource allocation and project efficiency.

Technologies: Requirements Engineering, Eraser.io, Notion.so, UML, Miro, MS Power BI, duckdb, PostgreSQL, DBeaver.

Requirements Engineering for workflow integration into Salesforce. Goal: Collect requirements to integrate existing manual workflows from Excel into Salesforce in order to fully automate them.

• Engineer Requirements for Desired Outcome: Mr. Gaertner spearheaded the process of gathering and defining the technical and business requirements for the Salesforce transformation, ensuring the new workflows align with organizational goals.
• Design and Lead Workshops: Organized and led workshops with various department heads and key users, utilizing UML to facilitate effective communication and visualization of current and desired workflows for Salesforce integration.
• Define Groups of Workflow Parts for Agile Transformation: Segmented the workflow processes into distinct groups, facilitating an agile approach to their transformation and integration into the Salesforce platform.
• Manage Stakeholders: Actively engaged with stakeholders, including senior management, IT teams, and end-users, to ensure their needs and concerns were addressed. Maintained regular communication to keep stakeholders informed and involved throughout the project lifecycle.
• Oversee Development and Implementation: Coordinated with the Salesforce development team to ensure that the workflows were accurately transformed and implemented according to the defined requirements.
• User Training and Feedback Incorporation: Conducted training sessions for end-users on the new Salesforce workflows and incorporated their feedback for continuous improvement of the system.
• Performance Monitoring and Optimization: Established metrics to monitor the performance of the new workflows in Salesforce, identifying areas for further optimization and enhancement.

Technologies: Salesforce, Requirements Engineering, Eraser, Excel, UML.

Cashflow Analysis Automation. Goal: Automate the cash flow analysis in order to have an automated daily overview instead of a monthly manual insight.

• Engineer Requirements for Desired Outcome: Mr. Gaertner led the initiative to define the technical and functional requirements for automating the cash flow analysis, ensuring the new system aligns with financial reporting objectives.
• Delegate and Monitor Work Packages: Assigned specific tasks and responsibilities to the project team, closely monitoring progress to ensure adherence to the project timeline and quality standards.
• Test and Affirm Results: Conducted thorough testing of the automated cash flow analysis system to confirm its accuracy and efficiency. Validated results against expected outcomes to ensure reliability and correctness.
• Stakeholder Engagement and Communication: Regularly engaged with key stakeholders, including finance managers and IT staff, to align the project with business needs and to incorporate feedback into the development process.
• Coordination with ERP Specialists: Collaborated with BMD ERP specialists to integrate the cash flow analysis tool with the existing ERP system, ensuring seamless data flow and functionality.
• Training and Documentation: Developed training materials and documentation for the new system, facilitating a smooth transition for users to the automated process.

Technologies: BMD (ERP), Requirements Engineering, Eraser, Monday.com, UML.

Restructuring of a Data Warehouse. Goal: The loading times of a 3 layered Data Warehouse took way too long. Through restructuring a reduction of >80% could be achieved.

• Designing the New Layer Structure of the Involved Databases: Mr. Gaertner meticulously planned and designed the new layered architecture for the databases involved in the data warehouse restructuring. This included defining the logical separation of data into staging, warehousing, and presentation layers. He focused on optimizing the database structure for enhanced data processing, retrieval efficiency, and scalability, ensuring it could handle increasing volumes of data and complex queries.
• Creating New ETL (Extract, Transform, Load) Pipelines for Each Layer: He developed new ETL pipelines tailored for each layer of the database structure. This involved creating processes for efficient data extraction from various sources, transforming the data as per business logic, and loading it into the appropriate layers of the data warehouse. Mr. Gaertner ensured that these ETL pipelines were automated, reliable, and capable of handling data inconsistencies and errors gracefully.
• Documenting the New Structure for Each Layer and Database Table: He took charge of creating comprehensive documentation for the new database layer structure and each database table. This documentation included detailed schemas, data models, data flow diagrams, and explanations of the transformations applied at each stage. The documentation also served as a guide for future maintenance, scalability considerations, and as a reference for other teams interacting with the data warehouse.
• Implementing Data Governance and Compliance: Mr. Gaertner also ensured that the new data warehouse structure adhered to data governance standards and compliance requirements. He integrated data quality checks and security protocols within the ETL pipelines and database layers.
• Testing and Validation of Data Integrity: He was responsible for the thorough testing of the new data warehouse structure. This included validating the integrity and accuracy of the data across different layers and ensuring the ETL processes functioned as intended.
• Collaboration with Stakeholders for Requirements Alignment: Mr. Gaertner actively collaborated with business stakeholders and IT teams to align the data warehouse restructuring with business needs and technical requirements. He facilitated workshops and meetings to gather feedback and make necessary adjustments.
• Training and Knowledge Transfer: He developed training programs and materials to educate the relevant teams about the new data warehouse architecture. This was crucial for ensuring smooth adoption and ongoing maintenance of the system.
• Performance Monitoring and Optimization: Post-implementation, Mr. Gaertner set up monitoring tools to evaluate the performance of the new data warehouse structure. He focused on continuously optimizing the system for better performance and efficiency.

Technologies: SQL, Microsoft SQL Server, Microsoft SQL Server Integration Services (SSIS), UML.

Developing of a Middleware (SQL x GraphQL). Goal: Connecting the SQL Data Warehouse (DWH) with the workflow management SAAS tool (Monday.com GraphQL API) in order to push data, enrich it, pull back to the DWH and spread the information where needed.

• Developing Efficient SQL Data Pipelines for Merging, Structuring Data: Implemented sophisticated SQL queries and procedures to merge data from multiple sources, ensuring data integrity and consistency. Focused on optimizing these pipelines for speed and efficiency, reducing processing time and resource usage. Included steps for cleaning, validating, and transforming data to ensure it meets the required format and quality standards for further analysis.
• Designing the Structure of the Data Flow to Ensure a Single Source of Truth: Established a centralized data management system to maintain a single, reliable source of truth, eliminating data discrepancies and duplications. Meticulously mapped out the entire data flow, from data ingestion to storage, ensuring transparency and traceability of data transformations. Incorporated data compliance and security measures to protect sensitive information and adhere to regulatory standards.
• Development of the Core Structure to Easily Load Data into the SAAS Platform as well as Retrieve Enriched Data and Write It Back to the Data Warehouse: Designed and developed integrations for seamless data loading into the SAAS platform, ensuring compatibility and efficiency. Implemented processes for enriching data within the SAAS platform, enhancing its value, and then efficiently retrieving it for further use. Developed a robust mechanism for two-way data synchronization between the SAAS platform and the data warehouse, ensuring that updates and changes are reflected across systems. Automated the process of data exchange between the SAAS platform and the data warehouse, minimizing manual intervention and reducing the risk of errors. Built the core structure with scalability in mind, allowing for future expansion and integration with additional data sources or platforms as needed. Included comprehensive monitoring and logging features to track data flow, identify bottlenecks, and troubleshoot issues promptly. Developed a user-friendly interface for managing the data exchange process, making it accessible to users with varying levels of technical expertise.
• Documentation and Training: Created detailed documentation and training materials to assist users in understanding and effectively utilizing the new data management system.

Technologies: Python, SQL, Pandas, GraphQL, Microsoft SQL Server, Microsoft SQL Server Integration Services (SSIS).

Web Scraper for Google and Instagram. Goal: Collect the Instagram names of >1.500 celebrities and after that the respective timestamps of their posts in order to analyze potential causality between fame and frequency of posting.

• Build an algorithm that automatically got the Instagram usernames of >1.500 celebrities.
• Developed a Scraper that went over the Instagram API and collected the timestamps of all respective posts.
• Exported them into suitable format for further analysis.

Technologies: Python, Selenium, Beautiful Soup, Proxy Server.

AWS Migration of Excel Report Consolidation System. Goal: To successfully migrate the Excel report consolidation process to the AWS cloud environment, ensuring the system is robust, scalable, and secure, while maintaining or improving current functionality.

• Stakeholder communication: identifying key stakeholders, holding meetings to understand their needs for the migration, and providing regular updates through various channels.
• Requirement Gathering: Led the collection and analysis of requirements for migrating to AWS, focusing on understanding the current system's limitations and identifying areas for improvement in the cloud.
• AWS Service Selection: Identified suitable AWS services like AWS Lambda, AWS RDS, and Amazon S3, considering data storage, processing power, and cost.
• Ensured the migration plan complied with relevant data security standards and cloud-based data storage and processing regulations.
• Defined requirements for system scalability in AWS, including auto-scaling capabilities and performance benchmarks, to accommodate different load patterns.
• Drafted requirements for the migration process, prioritizing data integrity during the transition to AWS.
• Addressed challenges related to data conversion and compatibility during the migration phase.
• Specified the requirements for how users would interact with the system after its migration to AWS, focusing on maintaining a user-friendly interface in the new cloud environment.
• Established criteria for ongoing monitoring and maintenance of the system within AWS, including tracking system performance and implementing error logging.
• Identified necessary tools and technologies for the migration and subsequent operation in AWS, like AWS CloudFormation for resource management and AWS CloudWatch for monitoring.

Technologies: Excel, AWS, UML.

Visual Basics macro for consolidating Excel reports. Goal: Automate the consolidation of two Excel reports that had to be done manually on a weekly basis.

• Analyzing all requirements of the solution.
• Development and testing of the macro.
• Implementation of the macro in the production system.

Technologies: Eraser, Excel, Visual Basics, UML.