Data engineer állások, munkák it programozás, fejlesztés területen

• Az IT termék vagy alkalmazás backlogjának kezelése és priorizálása
• Üzleti és technikai követelmények összegyűjtése és pontosítása
• User story-k, functional specificationök és acceptance criteria-k készítése
• Folyamatos együttműködés fejlesztői és tesztelői csapatokkal

• Development of a robust framework for generating and distributing work across all operations
• Creation of tools and frameworks for teams to design specific workflows
• Establishment of a comprehensive reference for a 360-degree view of customer data

• Act as the link between business stakeholders and technical teams translating business requirements into clear ServiceNow solutions
• Support clients in process analysis optimization and documentation in alignment with ServiceNow best practices

Design, develop, test, document, and deploy high quality Salesforce solutions leveraging the latest capabilities of the Einstein 1 Platform, Lightning Web Components, and modern Apex patterns...

Design of high quality Salesforce solutions leveraging the latest capabilities of the Einstein 1 Platform, Lightning Web Components, and modern Apex patterns...

• 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

• Planning and overseeing all phases of projects including discovery, development, testing, deployment, and handover
• Managing scope, priorities, milestones, dependencies, resources, quality, timelines, and budgets
• Maintaining clear and timely communication with clients and internal teams

• 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

Junior AI Data Engineer (graph modeling) Degree: Done. Next stop: Building the data infrastructure that powers the digital world. Everyone hypes up the front-end, but we know the truth-it's the data pipelines that do the heavy lifting. If you're a fresh Data Engineer hungry for massive scale, it's...

Grow personally as a key player in the global finance team by developing the system landscape, creating data pipelines and BI products that facilitate automation and delivery of complex recurring and ad-hoc tasks across various functions, including global supply chain, sustainability, research...

Grow personally as a key player in the global finance team by developing the system landscape, creating data pipelines and BI products that facilitate automation and delivery of complex recurring and ad-hoc tasks across various functions, including global supply chain, sustainability, research...

• Development, maintenance, and support of cloud-based data operation infrastructure and production data pipelines
• Design, implementation, operation, and optimization of Azure data services and Databricks workloads

Design, build, and continuously optimize ETL/ELT workflows that power our data landscape — from ingestion to delivery — with reliability and scale in mind....

• Data ingestion processes from various sources into the Data Lake
• Design, development and optimization of complex data pipelines for reliable data flow
• Build, development and maintenance of frameworks facilitating construction of data pipelines

• The data platform end to end: ingestion, storage, and the query paths that keep millions of records fast on screen. You design it, you build it, you run it in production.
• The platform layer, directly with the CTO: infrastructure, deployments, and the path to production for the whole team.

• Improve and extend the current analytics and AI infrastructure (built around Databricks)
• Drive the data platform by observing the fast Data and AI landscape and bringing in new ideas

• You design data pipelines for ingestion, curation, pseudonymization, and versioning.
• Furthermore, you implement reproducible training and finetuning builds for GenAI edge device models.
• You set up experiment tracking and artifact registries for model variants.

• Technikai menedzsment és fejlesztés a csapat felhőalapú adatplatformján és adatmodellein Azure Databricks környezetben
• A Data-Driven Project Modelling (DPM) motor fejlesztése és karbantartása DevOps legjobb gyakorlatok szerint
• Üzleti logika adatmegoldássá történő átalakítása multinacionális csapatban

• Design and develop normalized and denormalized data models to support reporting and data integration needs
• Apply dimensional modelling techniques to create efficient, scalable, and business-oriented data models
• Develop and maintain data transformations and models using SQL and dbt

• 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