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The Data Engineering Specialist designs, develops, and maintains scalable ETL/ELT pipelines using Python, Dagster, and dlt. The role involves integrating data into cloud-based lakehouse architectures on AWS or Azure to support analytics and AI initiatives.
Senior Data Engineer building data lakes, warehouses, and ingestion pipelines while leading cloud migration, infrastructure-as-code development, and technical architecture at Constellation Energy.
Graduate software engineer role on a Data Management Suite team building and scaling large-scale data pipeline infrastructure, including real-time data integration services and Flink kernel customization, based in Singapore.
Provision, configure, and maintain infrastructure and integrations for secure, scalable data ingestion from government agencies, managing connectors and network connectivity in a federated IT environment.
Builds and optimizes a global data platform for ingestion, governance, and pipeline management, focusing on scalable distributed systems and metadata synchronization to enhance productivity for data engineers/scientists.
Lead AI Analytics Engineer architecting data pipelines, vector databases, and multi-agent orchestration frameworks (LangChain/LangGraph) to power autonomous risk detection in Grab's Trust & Safety team using Python and LLMs.
Designs and maintains production-grade data pipelines for AI/ML and RAG systems, focusing on ingestion, transformation, and vector search infrastructure (pgvector, Pinecone). Ensures data governance, compliance, and quality for regulated sectors like healthcare and government.
Senior Data Engineer building AI-ready data pipelines, RAG/vector search infrastructure, and data governance solutions using Python, Airflow, Kafka, and vector databases like pgvector and Pinecone.
iGaming Ontario leads the Province of Ontario’s dynamic internet gaming (igaming) market that, in its third year generated $83 billion in total wagers, a 31% increase over the previous year. This resulted in $3.2…
Technical Product Manager owning the roadmap for an AI-first healthcare data platform—driving healthcare data ingestion (HL7, FHIR), canonical data modeling, workflow orchestration, and AI-ready pipelines in close collaboration with engineering teams.
Designs and builds a Kafka-based medical services platform to ingest, process, analyze, and securely store high-volume cardiac data for clinical analytics and reporting.
Contract Senior Data Engineer onsite in London, building and migrating enterprise data pipelines from Azure Data Factory and ADLS to Microsoft Fabric for a financial-services transformation programme.
About ARSIEM Corporation At ARSIEM Corporation we are committed to fostering a proven and trusted partnership with our government clients. We provide support to multiple agencies across the United States Government.…
Builds and maintains risk analytics platforms by developing calculation engines, data pipelines, and backend services for financial risk assessment, collaborating with quants and risk specialists.
Data Engineer role focused on building and optimizing ETL/ELT pipelines and large-scale data processing frameworks using Python (Pandas, NumPy) with CI/CD and cloud (Azure) in Spain.
Builds and maintains enterprise-grade data pipelines using Python, Pandas, and NumPy to ingest, process, and transform large datasets for scalable analytics and business insights.
Data Engineer focused on security analytics at Allianz Commercial, building dashboards, KPIs/KRIs, and data pipelines using Excel and Power BI while integrating data from SIEM, EDR, IAM, and other security systems within the Digital Resilience as a Service framework.
Overview Who we are Collaborative. Respectful. A place to dream and do. These are just a few words that describe what life is like at Toyota. As one of the world’s most admired brands, Toyota is growing and leading the…
The Lead Software Engineer will design and develop real-time risk and PnL applications for Rates trading desks using Python. The role involves building high-performance services, managing production stability, and driving the adoption of AI-assisted engineering practices within the team.
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