Data Engineer
Develop and maintain data pipelines and architectures using Microsoft Fabric and Azure technologies. Ensure high standards of data quality and governance while collaborating with cross-functional teams to deliver innovative solutions.
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Develop and maintain data pipelines and architectures using Microsoft Fabric and Azure technologies. Ensure high standards of data quality and governance while collaborating with cross-functional teams to deliver innovative solutions.
Own and scale the central database and knowledge graph that serves as the memory for AI agents and product features. Design ingestion pipelines for funding rounds and market news while ensuring end-to-end data quality and entity resolution.
Design and deploy scalable data ecosystems and pipelines while integrating and modeling data into platforms. Develop intelligent applications and use cases to improve client efficiency and revenue generation.
The role involves working as a Data Engineer within a dynamic team of analytics experts. Responsibilities include data integration, ETL processing, and managing big data environments.
The role involves reinforcing the data engineering team by managing large volumes of data and utilizing the Elastic ecosystem. The engineer will work on innovative international projects focusing on data administration and system maintenance.
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The role involves managing data collection and ingestion pipelines to support large-scale model training operations. You will collaborate with scientists to optimize infrastructure for cost, throughput, and data quality.
The Lead Data Engineer will design, develop, and maintain robust Spark applications while enforcing coding standards and best practices across the project. They will also collaborate with cross-functional teams to optimize performance and ensure the reliability of enterprise-level data solutions.
The Data Engineer will design, develop, and optimize scalable ETL/ELT processes and data solutions within Google BigQuery. They are responsible for ensuring data consistency, reliability, and auditability across financial and operational reporting domains.
You will build and maintain backend services and data pipelines to power marketing intelligence and automation platforms. This involves developing ETL/ELT workflows, managing microservices, and ensuring high-quality, production-ready code through CI/CD and observability practices.
The Senior Clinical Data Engineer will design, develop, and maintain scalable data pipelines to process and transform clinical and medical device data. They will collaborate with cross-functional teams to ensure data quality, consistency, and usability for statistical analysis and evidence generation.
Own and deliver the year-end financial audit data workstream, including transactional data ingestion and reconciliation. Collaborate with accounting stakeholders to resolve data discrepancies and build robust reporting pipelines in Snowflake and Looker.
You will own and improve monitoring, observability, and engineering standards across the data platform to ensure reliable pipeline health. Additionally, you will build internal tooling and support AI adoption to remove friction for data analysts and engineers.
The role involves managing data collection and ingestion pipelines to support large-scale model training operations. You will collaborate with scientists and leadership to optimize data infrastructure and define the dataset roadmap for AI products.
Design and build large-scale distributed data processing systems while operating self-service platform features for product engineers. Maintain mission-critical data pipelines and ensure architectural consistency through code reviews and technical mentorship.
The role involves managing data collection and ingestion pipelines to support AI model training at scale. You will collaborate with scientists to optimize infrastructure and develop a roadmap for dataset acquisition.
Design, build, and maintain streaming data pipelines while migrating existing ingestion processes to internal infrastructure. Ensure data consistency and resilience within the Snowflake warehouse while evolving the AWS streaming stack.
Design and develop Python-based backend services and APIs integrated with graph databases to enable advanced analytics. Build and maintain scalable data pipelines and reusable data products while ensuring data quality and governance.
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Design, implement, and scale production-grade data pipelines and real-time streams ranging from megabytes to petabytes. Lead technical projects through architecture and implementation while collaborating with cross-functional teams to maintain data quality.
Build and evolve a cloud-native, AI-first data platform that transforms business data into actionable assets for AI agents and humans. Lead the transition from batch-oriented pipelines to real-time streaming systems while improving data observability and governance.
Design and implement scalable data warehouse solutions and advanced data pipelines to facilitate a data-driven culture. Collaborate with cross-functional teams to translate business needs into technical requirements and lead code reviews.
Design and implement scalable data warehouse solutions and advanced data pipelines to facilitate a data-driven culture. Collaborate with cross-functional teams to translate business needs into technical requirements and lead code reviews.
The Data Engineer will lead a quality-driven migration of data pipelines, focusing on improving structure and reliability rather than a simple lift-and-shift. Key tasks include mapping dependencies, ensuring PII compliance, and implementing robust data quality tests and monitoring.
Lead the quality-driven migration of data pipelines by engaging with dataset owners and optimizing existing queries. Establish data quality tests, monitoring infrastructure, and ensure security compliance for PII fields.
Design, implement, and maintain scalable data pipelines on the Databricks Lakehouse Platform using Apache Spark and Delta Lake. Collaborate with cross-functional teams to optimize data workflows and support data governance and security best practices.
Develop and maintain robust data pipelines and analytical data models using SSIS and SSAS. Build reporting solutions with Power BI and SSRS while optimizing SQL Server performance and ensuring data quality.
Build and maintain data pipelines for collecting, transforming, and integrating data from various sources. Ensure efficient data enablement and reporting solutions through the deployment of scalable data products.
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The role involves designing and optimizing data flows within a Fabric Lakehouse environment, focusing on low-latency processing and multi-stack integration. Responsibilities include implementing data design patterns like SCD and managing transaction logs for Delta Lake.
Develop and maintain scalable data pipelines and manage solutions within the Microsoft Fabric Lakehouse environment. Collaborate with Data & AI teams to optimize cloud-based data solutions and ensure governance standards.
The candidate will develop and maintain ETL/ELT pipelines using Azure Data Factory, Databricks, and PySpark. They will also support data preparation and validation within Data Lake and Lakehouse environments.
Lead the design and implementation of end-to-end cloud-native data architectures, including ingestion, modeling, and orchestration. Provide technical leadership by mentoring engineers, optimizing pipelines, and communicating complex decisions to stakeholders.
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