17.11.2025 aktualisiert
60 % verfügbar
Architect Data, Science & Analytics
Mering, Deutschland
Weltweit
Diplom MathematikSkills
AirflowDatenbankenContinuous DeliveryContinuous IntegrationData ArchitectureETLData MartData MiningData VaultData WarehousingR (Programmiersprache)Jinja2PythonPostgresqlUnix-ShellMachine LearningMonte-Carlo-SimulationOracle FinancialsParallel ComputingPerformance-TuningSasSas/basePL/SQLSQLStreamingSystemtestsTableauTalendTransaktionsdatenInformatica PowercenterSnowflakeIT-ArchitekturGitlabTemplatingKubernetesDeployment AutomationDatenmanagementCloud-MigrationDaten-PipelineDocker
Apache Airflow, cloud migration, Continuous Deployment, Continuous Integration, CI/CD, data architecture, master data management, data mart, data extraction, data pipeline, data flows, data flow, Data Vault, Data Warehouse Architecture, data warehouse, database, automated deployment, Docker, ETL, Gitlab, IT architecture, Informatica PowerCenter, jinja, Kubernetes, machine learning, Monte Carlo simulation, Oracle, PLSQL, parallel processing, Performance optimization, Postgres, PostgreSQL, Python, R, SAS, SAS Data, SAS Base, SQL, SQL queries, Snowflake, System Test, Tableau, Talend, templating, transactional data, Shell
Sprachen
DeutschMutterspracheEnglischverhandlungssicher
Projekthistorie
Data Warehouse Architecture
- Alignment of business strategy with C-Level to understand their goals, objectives, and strategies.
- Collaborating closely with software and platform architects to develop a cohesive and integrated solution.
- Developing IT architecture and IT roadmap suitable to the business strategy
- Establishing and enforcing architectural standards and guidelines to ensure consistency and interoperability across the organization's IT landscape.
- Facilitating change management processes to ensure smooth transitions during the implementation of new IT initiatives or changes to existing systems
- Defining and implementing the data pipeline, orchestrating its journey from the source systems to the final reports with Apache Airflow on Kubernetes.
- Establishing fundamental concepts such as restart ability, historization, and alert mechanisms to efficient error recovery, data tracking, and timely alerts for potential issues.
- Defining and implementing workflow schedules and workflow trigger with Apache Airflow, implementation of reusable tasks and task groups
- Implementation of Helm Charts and Helmfiles for automatic deployment on Kubernetes
- Facilitating workshops with the data team to explore innovative approaches, integrate feedback, drive continuous improvements, and foster knowledge exchange. These sessions serve as collaborative forums to explore new strategies, incorporate team insights, and enhance the collective expertise within the data team.
- Evaluation of Snowflake for the data warehouse platform as part of cloud migration project. Implementing a proof of concept.
- Establishing an automated deployment strategy through Continuous Integration/Continuous Deployment (CI/CD).
- Establishment and conception of development guideline
- Development of generic ETL processes with python.
- Assessing and identifying the most appropriate modeling method tailored to the specific needs and objectives of the company. This evaluation involves analyzing various modeling approaches to determine the optimal fit for the organization's data architecture and future scalability.
- Conceptualizing, designing, and implementing the data model.
- Establishing a templating methodology to dynamically generate data vault tables and procedures in PostgreSQL using Python. This innovative approach streamlines the creation of data vault components based on defined jinja templates.
- Expanding the foundational methodology to incorporate deletion strategies and effective satellite tables.
- Analyzing business requirements to determine their feasibility and complexity, fostering discussions to refine layouts within the same business context, thereby enhancing the implementation process in Tableau for optimal outcomes.
- Development of trigger by tableau extract refreshes, CI/CD deployment of data source and workbooks including Python scripting.
- Crafting data mart structures that align with performance-oriented and sustainable principles, ensuring a robust and efficient implementation within Tableau's framework.
- Definition and implementation not role-based strategies and access rights concepts tailored specifically for Tableau projects and reports.
- Designing and developing Tableau reports while conceptualizing a robust reporting framework ensuring aligned implementation with business requirements.
Development of Tableau Reports
Implementation of Residual Value
- Alignment of business requirements and conduction of workshops for technical concepts
- Development of report definitions in close collaboration with the department.
- The implementation/extension of the associated data flows in oracle, SAS and Informatica Powercenter.
- Performance improvements of Tableau report with data over 20 Mio records.
- Creation of technical documentation.
- Conduction of Technical and System Test and fixing of defects of unit/business test.
Implementation of Residual Value
- Defining master data management tables to configure residual value at risk, incorporating stress adaptations as specified by the business.
- Designing and developing transactional data tables within the Oracle environment. This process entails creating optimized tables to efficiently store and manage transactional data critical for risk assessment and calculation processes.
- Implementation of the statistical methods of estimation the residual value risk
- Architecting and developing the data flow infrastructure in SAS to facilitate the calculation of Residual Value at Risk.
- Migration of database from on-premise Oracle to Snowflake
- Collating requirements from diverse departments regarding relevant data and report design specifics.
- Streamlining data requirements, aligning business definitions, and fostering interdepartmental discussions for coherence.
- Architecting the business data model to intricately map the organization's data landscape
- Developing and conducting a comprehensive two-day training program centered on business data modeling.