Python programmer állások Budapesten (adatrögzítő)

• Translation of business requirements into KPIs, metrics and visualizations
• Building and maintaining semantic and analytical data models on top of the data warehouse
• Writing and optimizing SQL queries, views and calculated measures for reporting use cases

Design, develop, and maintain interactive Power BI dashboards and reports to support spend analysis, supplier performance, contract lifecycle management, and procurement KPIs...

• Development of dashboards
• Support of data integration
• Analytical investigations
• Identification of trends, risks and opportunities
• Development of reporting solutions
• Provision of data analysis, reporting and performance insight
• Improvement of reporting capability and automation

• Aktuárius gyakornokként nem-élet aktuáriusi, riportálási feladatokban vehetsz részt
• Pénzügyi gyakornokként tapasztalatot szerezhetsz a klasszikus és biztosításspecifikus számvitel és adózás, továbbá a biztosító treasury területén

• Establish data feeds with custodian banks, data vendors, and other service providers
• Support senior leadership, clients, and the broader Data Team in executing analyses and providing data-driven answers to questions
• Fully automate and centralize existing data integration processes

• Leading and developing the three-person QC function
• Defining daily and weekly QC priorities
• Training and coaching QC specialists
• Ensuring consistent handling of complaints, QC findings and production issues
• Maintaining consistent QC practices across locations, projects and production teams

• Configuration of the P&P platform end to end across 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 for non-standard configurations

• 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

• Implementation of end-to-end analytics and dynamic visualizations in Tableau, ThoughtSpot, SAP Analytics Cloud, or equivalent enterprise BI platforms
• Preparation, transformation, and modeling of data using SQL, Python, or dbt leveraging enterprise cloud environments like Snowflake and Microsoft Azure

• Design and implement enterprise-scale data pipelines and advanced analytics dashboards
• Develop and optimize data ingestion, transformation, and modeling workflows across structured data environments and cloud-based platforms

• Support the migration of ETL pipelines between source systems and Snowflake
• Use SQL to validate data, check data quality, reconcile datasets, and identify discrepancies
• Monitor pipeline executions, investigate issues, and help resolve technical blockers

• Operational efficiency improvement through process optimization, automation, and adoption of new technologies
• Understanding the company's data warehouse and aligning data flows with reporting requirements
• Development and maintenance of reports and data-driven solutions using Power BI and Google Sheets

• Support of IP and optical network capacity planning including traffic growth analysis and demand forecasting
• Analysis of utilization, throughput, latency, and error metrics from network elements and links

• Design and implement modern data architectures and lakehouse solutions
• Provide technical leadership throughout the project lifecycle
• Collaborate with business stakeholders
• Create effort estimations
• Provide technical expertise during the pre-sales phase
• Mentor 3-6 person project teams

• Experience in different Data Science topics
• Independent delivery of Data Science projects with e2e product-oriented mindset
• Direct engagement with clients to understand business objectives and translate into technical requirements
• Leading pre-sales technical discussions

• Design and implement pipelines to ingest, clean, and transform data into Data Lakes and Data Warehouses
• Active monitoring and optimization of pipelines for performance and cost
• Configuration of pipeline deployments and management of environment settings using IaC tools and CI/CD