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Job Description Data Platform Ownership: Build, maintain, and drive the transition to our new Data Platform (Dagster, dbt, AWS ECS, and GCP GBQ). You will build a product that acts as a foundation for other data teams,…
Stellenbeschreibung Data Platform Ownership: Du baust und wartest unsere neue Datenplattform (Dagster, dbt, AWS ECS und GCP GBQ) und treibst den Übergang dorthin aktiv voran. Dabei entwickelst du ein Produkt, das als…
The Lead Software Engineer will lead a cloud data engineering team to design and build scalable data services for provider data management in the healthcare sector. The role involves hands-on development, implementing TDD and CI/CD practices, and integrating AI tools to optimize data pipelines and team productivity.
Koniag Advisory and Business Solutions, LLC a Koniag Government Services company, is seeking a Cloud Data Architect (Snowflake), SME to support KABS and our government customer in Washington, DC. The position is…
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.
Builds and maintains Snowflake-based data warehouse pipelines, using dbt and SQL to enable self-service analytics for fintech teams.
Analytics Engineer migrating the analytics warehouse to Snowflake and improving ETL/ELT processes, collaborating with analysts and data scientists using SQL and Python.
Onsite Python Data Engineer in Birmingham building data models, ETL pipelines, and Tableau dashboards to support audit and risk compliance processes.
Data Engineer (remote/UK-based) building and improving data pipelines, migrating to Microsoft Fabric, and working with SQL Server and Azure in a small collaborative team.
Early-career Full Stack Engineer working across backend Python services, APIs, data pipelines, and cloud environments (AWS/Azure), supporting production applications and client projects in a hybrid London role.
Senior Data & AI / MLOps Engineer responsible for industrializing and deploying ML models, LLMs/AI agents, and data pipelines into production within the financial sector, using Python, PySpark, AWS, Docker, Kubernetes, and MLflow.
Builds and maintains ETL pipelines in a hybrid role, focusing on cloud systems and high-quality Python/SQL code for AI and fintech data infrastructure.
Work on scalable data solutions, cloud architectures, and Generative AI, focusing on transforming data into business value and operating in production environments.
Senior Data Engineer focused on LLM & RAG architectures for a major telecom group, designing scalable data pipelines, optimizing vector databases and RAG solutions, and building AI services with FastAPI using Python, SQL, Spark/PySpark on Azure/Databricks.
Senior Big Data Engineer specialized in Databricks, leading Lakehouse architecture design, ETL/ELT pipeline optimization, and data governance standards for a digital services consultancy in Madrid or Barcelona.
Senior Data Engineer building and maintaining ETL/ELT pipelines, ingesting structured and unstructured data, and developing RAG systems using Python, SQL, Kubernetes, and cloud platforms (Azure/AWS) for an AI Fintech in Madrid.
Lead Data Engineer designing and overseeing end-to-end data pipelines (ingestion, transformation, loading) on the SimpliFi Data Pool—an Azure-hosted repository using Data Vault 2.0, Databricks, Azure Synapse, Informatica IDMC, and PowerBI—while mentoring engineers and enforcing data governance.
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.
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