The Lead Data Engineer will assess data sources and availability to determine the technical feasibility of AI, automation, and reporting use cases. They will identify integration requirements, manage implementation risks, and provide technical documentation and sizing for build activities.
Remote Data Engineer Jobs (2,730)
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Build and manage end-to-end data pipelines in Snowflake to power internal AI platforms and products. Collaborate with ML engineers and data scientists to translate requirements into scalable data models and AI-ready infrastructure.
Define the multi-year vision for Data Governance and Quality while designing scalable data architectures for real-time and AI requirements. Lead cross-functional task forces to ensure compliance with regulatory landscapes like GDPR and the AI Act.
Data Engineer with Python and React - Remote
Glint Tech Solutions LLC
·
Full Time
·
7 Weeks Ago
Glint Tech Solutions LLC
Build and maintain scalable data-driven web applications and ETL pipelines using Python and React within the healthcare domain. Design internal tools and analytics dashboards while ensuring compliance with security and privacy standards like HIPAA.
Design and maintain scalable data pipelines using SparkSQL and PySpark to ingest and transform IoT data into a core data model. Collaborate with cross-functional teams to provide high-quality data for statistical analysis, model training, and dashboarding.
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Design and maintain scalable data pipelines using SparkSQL and PySpark to ingest and transform IoT and software data. Collaborate with cross-functional teams to provide high-quality data for statistical analysis, model training, and dashboarding.
Build and optimize ETL pipelines to transform raw data into clean, actionable models for business insights. Partner with cross-functional teams to design sustainable data infrastructure for marketing attribution and customer analytics.
Define the technical architecture and platform standards for a modern AWS lakehouse to support cross-product analytics, ML, and AI. Lead the design and delivery of production pipelines while mentoring engineers and establishing data governance standards.
Design and optimize complex data storage solutions and automate scalable ETL pipelines. Collaborate with cross-functional teams to deliver high-quality datasets and APIs for GenAI-powered products.
Design and maintain data pipelines to ingest industrial and historian data into GCP, specifically using BigQuery and Bigtable. Bridge the gap between operational mining systems and cloud platforms to support AI-driven optimization models.
Support a large-scale data modernisation programme by migrating analytics platforms from Oracle to Microsoft Fabric. Responsibilities include designing data pipelines, implementing Microsoft Purview for governance, and developing semantic models within an Agile environment.
Support the migration of an analytics platform from Oracle to Microsoft Fabric by designing and developing data pipelines and semantic models. Implement data governance and cataloguing using Microsoft Purview within an Agile delivery environment.
Build reliable data streams for product analysis, compliance reporting, and operational dashboards. Design and maintain complex data pipelines and legacy codebases while bridging communication between technical and non-technical teams.
The role involves designing and optimizing enterprise-scale data center infrastructure to ensure scalability, security, and efficiency. The engineer will lead complex multi-team initiatives, manage vendor relationships, and drive operational excellence through automation.
Build and activate company-wide data assets in Snowflake by designing efficient data models and pipelines using DBT. Collaborate with stakeholders to implement metrics and dimensions that enable self-service analysis and trusted KPI reporting.
Design, develop, and maintain scalable ETL/ELT pipelines and modern lakehouse architectures using Databricks and AWS. Collaborate with stakeholders to build high-quality data models and ensure data accuracy and reliability.
Lead the development of a trustworthy analytics data layer by creating clean dimensional models and optimizing data pipelines. Enable self-serve analytics for business users and provide mentorship to other data engineers and analysts.
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Lead the large-scale migration of legacy documentation into a cloud-based environment, focusing on transforming unstructured data into structured formats. Design and maintain MuleSoft integrations and Salesforce Data 360 implementations to ensure seamless data flow and harmonization.
Design and maintain core services, APIs, and ingestion systems that power the AI data platform. Collaborate cross-functionally to transform complex real-world data into scalable, reliable systems for AI models.
Architect and build modern, cloud-native data platforms on Google Cloud using BigQuery as the analytical backbone. Lead the design of unified batch and streaming pipelines while mentoring engineers and establishing data governance practices.
Lead the design and implementation of modern end-to-end data architecture and intricate processing pipelines to meet business objectives. Manage projects from inception to completion while collaborating with stakeholders and mentoring technical teams.
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.
Design 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 implement best practices for data governance and security.
Design 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 implement best practices for data governance and security.
Design, build, and operate scalable infrastructure for ingesting and processing petabytes of data into a lakehouse. Develop internal libraries and tooling to support AI, analytics, and data discovery across the organization.
Design, build, and operate scalable infrastructure for ingesting and processing petabytes of data into a lakehouse. Develop internal libraries and tooling to improve the reliability and observability of the Spark and Databricks-based data platform.
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The candidate will be responsible for designing and maintaining data pipelines as a Senior Data Engineer. They will ensure the efficient flow and processing of data across the organization.
Design and implement scalable cloud-based data platforms and pipelines to power analytics, AI, and GenAI systems. Collaborate with cross-functional teams to ensure data governance, observability, and the delivery of production-grade data products.
Design and implement scalable cloud-based data platforms and pipelines to power analytics, AI, and GenAI applications. Collaborate with cross-functional teams to ensure data governance, observability, and the delivery of production-grade datasets.
The Lead Data Engineer will assess data sources, quality, and integration requirements to validate the technical feasibility of AI and reporting use cases. They will provide platform recommendations, identify implementation risks, and support technical documentation for final recommendations.
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