Python developer állások, munkák adatbázisszakértő kategóriában

• Design and build scalable, reusable frameworks and services for data ingestion, transformation, and processing
• Develop and maintain the platform’s core capabilities such as orchestration, data quality validation, and schema management tools

• Tackling complex technical challenges focused on scalability, reliability, and speed
• Maintaining a high-performance infrastructure that processes millions of events with low latency
• Collaborating with Data Scientists and Engineers to design and implement cutting-edge feature topologies

• Ownership of data engineering features, architecture, and code quality
• Design, implementation, and maintenance of Databricks-based data pipelines and workflows
• Building and optimization of ETL/ELT processes using Apache Spark on Databricks

• Delivery of complex data engineering projects across industries
• Leading small delivery teams
• Management of customer relationships alone or with a Project Manager
• Supporting pre-sales process
• Mentoring more junior engineers
• Contributing to hiring best-in-class data talent

Performing tasks related to the position....

• 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

• 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

• 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.

• 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

• analyze complex databases, data streams, and large datasets to derive valuable insights and actionable recommendations
• develop, implement, and optimize algorithms and analytical models to improve existing business processes

• 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

• Advise business stakeholders on data-driven opportunities and solutions that support informed decision-making, digitalization, and business growth
• Design end-to-end data solutions for business use cases, from data ingestion and transformation to reusable data products

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

• Develop, maintain and optimize Python and Linux shell scripts for data extraction, transformation, and loading.
• Build automated pipelines and reusable code modules to support recurring reporting and analytics workloads.
• Ensure accuracy, completeness, and consistency of datasets.

Customer models, end to end. Turn a planning problem, with its capacities, costs, lead times and shift plans, into a model whose answer a plant manager acts on. That includes the data it runs on: ERP and Excel exports, and catching the numbers that cannot be right before the customer does....

Drive Architectural Strategy & Scalability: Partner across Data Science, Platform Engineering, and Product teams to design the technical architecture, data pipelines, and orchestration systems required for high-traffic AI services. Ensure the ecosystem supports highly available, low-latency...

The mathematics behind the models. A model means exactly one thing, and it still means that after it reaches a solver. The algebra underneath is yours, and so is the call on what the engine should be able to express next and what it should refuse....

• 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.

Design, build, and maintain scalable data platforms and data & AI solutions tailored to business needs, including the development of governance structures and access control concepts...

• 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