22 results
FiltersDesign, build, and maintain scalable batch and real-time data pipelines that power analytics, experimentation, and machine learning. Partner cross-functionally with analytics, product, engineering and operations to deliver high-quality data solutions that drive measurable business impact. Develop and maintain curated, well-modeled datasets that serve as trusted sources of truth across the organization.
Design, build, and maintain scalable batch and real-time data pipelines that power analytics, experimentation, and machine learning. Partner cross-functionally with analytics, product, engineering and operations to deliver high-quality data solutions that drive measurable business impact. Develop and maintain curated, well-modeled datasets that serve as trusted sources of truth across the organization.
Shape and evolve the backbone of Blip's real-time data processing layer. Design and implement streaming and batch data systems that move and transform information reliably and efficiently across Blip’s platform. Champion data modeling excellence, ensuring high-quality, discoverable data for real-time consumption. Key responsibilities include data engineering & pipeline architecture, modeling & storage, and reliability, quality & observability.
Shape and evolve the backbone of Blip's real-time data processing layer. Design and implement streaming and batch data systems that move and transform information reliably and efficiently across Blip’s platform. Champion data modeling excellence, ensuring high-quality, discoverable data for real-time consumption. Key responsibilities include data engineering & pipeline architecture, modeling & storage, and reliability, quality & observability.
Excella is looking for a Senior Consultant Data Engineer to play a key role in designing and building modern data solutions. They will develop robust, scalable, and sustainable data pipelines using batch and streaming technologies. The Senior Data Engineer will also collaborate with cross-functional teams to ensure data availability, quality, and integration.
This role is ideal for a technically skilled leader passionate about building scalable, high-performance data platforms, leading a team to design and maintain pipelines for millions of daily events, ensuring data reliability and accessibility, owning the architecture and optimization of a cloud-based data warehouse, and influencing the development of a Customer Data Platform.
The Data Engineering team is responsible for designing, building, and maintaining the Data Lake infrastructure, including ingestion pipelines, storage systems, and internal tooling for reliable, scalable access to market data. Implement and tune data storage for petabyte‑scale analytics and collaborate with Data Science, Quant Research, Backend and DevOps to translate requirements into platform capabilities.
Architect, build, and operate real-time/batch ETL pipelines, agentic orchestration flows, and AI/ML endpoints for autonomous, multi-agent production systems. Contribute actively to team processes, documentation, and operational quality. Build event-driven data workflows, integrate with various connectors, and expose agentic features.
This role involves collaborating with clients to understand their IT environments and digital transformation goals. Responsibilities include collecting and managing large volumes of data, creating robust data pipelines for Data Products, and defining data models that integrate disparate data. The role also includes performing data transformations using tools such as Spark, Trino, and AWS Athena and developing, testing, and deploying Data API Products with Python and frameworks like Flask or FastAPI.
At Vattenfall, the mission is to help customers power their lives in climate smarter ways and enable a fossil free future. As a Data Engineer in the Data Science team, you will join a collaborative and skilled team that drives automation of real business cases through data-driven and Machine Learning-based intelligence. You will design, maintain, and monitor real-time and batch data pipelines, ingest and process external data feeds, and collaborate with Data Scientists and ML Engineers to build and maintain feature pipelines.