Data engineer állások Budapesten (it programozás, fejlesztés)

a projektvezetők munkáját operatív és szakmai oldalról támogatod a projektek előkészítése, tervezése, nyomon követése és koordinációja során...

• Design of high quality Salesforce solutions leveraging Einstein 1 Platform, Lightning Web Components, and modern Apex patterns
• Development of high quality Salesforce solutions leveraging Einstein 1 Platform, Lightning Web Components, and modern Apex patterns

• Design, develop, test, document, and deploy high quality Salesforce solutions leveraging Einstein 1 Platform, Lightning Web Components, and modern Apex patterns
• Analyze client specific requirements and define data model to support business, reporting and technical requirements

• Research, configure, and test AI tools including Claude, Gemini, Zapier, and n8n to improve the GTM stack
• Build proof-of-concepts for AI-driven lead research, automated email drafting, meeting summarization, and information hand-offs

• Owning a revenue target across data, advanced analytics, and predictive AI
• Leading executive-level client conversations to diagnose business problems, structure solutions, and build ROI cases

• Data pipeline development
• Data integration
• Data warehousing
• Data modelling
• Data quality and governance
• Performance optimization
• Collaboration with data scientists and analysts
• Technology selection
• Security and compliance
• Monitoring and troubleshooting

• Design, develop, and implement complex, scalable Lakehouse solutions on the Databricks platform across major cloud environments (Azure, AWS, or GCP)
• Design, build, and optimize modular data pipelines using Delta Lake to ensure data efficiency, reliability, and quality

• 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

• Managing and migrating ETL pipelines between various source systems
• Validating pipeline build success and output data quality
• Communicating with business stakeholders and SME's regarding their respective data domain
• Analysing and resolving blockers and issues with ETL pipelines and data discrepancies

• Development and operation of data pipelines
• Building data layers in OneDataLake (ODL) Central Data Store Consumer (CDSC)
• Balancing business and IT demands to build modern and efficient pipelines in public cloud environments
• Integration of data preparations into managed processes

• Understanding and aligning with business needs
• Identifying related data requirements
• Participating in technical pre-sales activities
• Effort estimation
• Proposal creation
• Architecting and planning robust data models
• Architecting and planning modern data platforms

• Technical leadership and mentoring of data engineers
• Technical oversight for Databricks solutions
• Design and implementation of scalable ETL/ELT pipelines using Databricks, Python/PySpark, SQL, and Delta Lake

• Lead the design and implementation of scalable data pipelines and Lakehouse architectures
• Act as the design authority
• Solve the hardest technical challenges including optimizing high-load streaming pipelines, debugging complex Spark jobs, and designing generic frameworks

• 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

• Experience in different data science topics including machine learning, LLM, deep learning
• Independent delivery of data science projects with e2e product-oriented mindset
• Direct engagement with clients to understand business objectives and translate into technical requirements

• Build and maintain dataset ingestion, cleaning, and versioning pipelines
• Design and operate labeling workflows including quality control and inter-annotator agreement tracking
• Own dataset testing including coverage analysis, class balance, leakage detection, and dataset documentation

• 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

• Understanding global business strategy and translating on business department/employee level
• Creating alignment within the organisation for action plan implementation
• Proactive testing of new solutions and proposals to leverage service delivery maturity

• Business process analysis and design of Lead-to-Order processes
• Cross-country process harmonisation
• Requirements engineering including user stories and acceptance criteria
• Business workshops and stakeholder alignment
• Fit-gap and impact analysis
• End-to-end solution definition

• Creation and support of business intelligence solutions
• Driving BI requirements with clients
• Capturing accurate reporting documentation
• Communicating at very high level to internal and external stakeholders