2,084 Data Engineering jobs available for remote work from home. Apply for positions such as Data Scientist, Data Engineer – Cybersecurity, GRC & AI, Business Intelligence Analyst (Real Estate), and more! Discover the best work-from-home or hybrid, full- and part-time jobs.
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You will partner with cross-functional teams to turn ambiguous business questions into rigorous analyses, models, and experiments. You will own the end-to-end process from framing questions to landing actionable recommendations with leadership.
Develop and maintain scalable data pipelines and models to support cybersecurity, GRC, and AI-assisted workflows. Collaborate with cross-functional teams to ensure data reliability, audit readiness, and effective integration with enterprise platforms.
The Business Intelligence Analyst will query data to build and maintain recurring reports, dashboards, and KPI trackers for the organization. They will also leverage AI tools to automate data preparation and provide actionable insights to department leaders and executives.
The Senior Manager will define the enterprise-grade data platform roadmap, architecture, and governance framework to support business and revenue objectives. They will collaborate with engineering and IT teams to build secure, compliant data pipelines and integrate advanced GenAI-driven analytics.
The Senior Database Engineer will design, implement, and manage data architectures across GCP and Azure while collaborating with cross-functional teams. They are responsible for optimizing production database systems, ensuring high availability, and supporting strategic data migration and modernization initiatives.
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The Senior Data Engineer will design, develop, and deploy data solutions, including data models, dashboards, and reports to provide business insights. They are responsible for managing data ingestion, ETL processes, and performance tuning of complex SQL and DAX queries.
You will design, build, and optimize scalable data pipelines and modern data platforms to enable advanced analytics and AI-driven decision-making. Additionally, you will collaborate with client stakeholders to translate business requirements into technical data solutions while supporting data governance and quality initiatives.
Lead a team of data engineers to design and build scalable data pipelines using a modern lakehouse stack. Manage stakeholder relationships and drive operational excellence while championing AI-forward development practices.
Design, build, and maintain robust ETL/ELT pipelines to integrate data from various sources into Snowflake. Collaborate with product and business leaders to transform data into actionable insights and automate workflows using AI tools.
You will build and maintain data platforms for Finance solutions using Microsoft Fabric, managing the entire lifecycle from ingestion to reporting. Additionally, you will support agent builds and collaborate with delivery teams to ensure production pipeline health and documentation.
The Senior Data Engineer will lead the implementation of modern data engineering solutions using Microsoft Fabric and the Azure data platform. They are responsible for building and maintaining scalable data pipelines, integration processes, and analytics-ready platforms while ensuring data quality and governance.
The Senior Data Engineer will serve as the technical owner of the enterprise data platform, overseeing the design, development, and optimization of Snowflake and dbt transformation frameworks. They will lead the creation of scalable ELT pipelines and ensure data integrity, quality, and governance across the organization's data assets.
You will serve as the technical anchor for a squad, driving cross-boundary architectural decisions and ensuring the integration of AI, backend, data, and frontend components. Additionally, you will conduct code reviews, mentor senior developers, and translate high-level product requirements into actionable technical tasks.
The Lead Director will oversee the strategy, architecture, and delivery of enterprise AI platform capabilities and reusable data products. This role involves leading high-performing engineering teams to scale AI/ML solutions while establishing operational standards for governance, security, and platform reliability.
The Lead Data Engineer will own the architecture and evolution of the enterprise data platform, including Snowflake data warehouse and ETL/ELT pipelines. They will establish engineering standards, mentor team members, and partner with stakeholders to deliver reliable data solutions.
The Sr. Software Engineer will design, develop, and deploy end-to-end data integration solutions for clinical reporting platforms while driving the adoption of AI tools. They are also responsible for providing production support, resolving complex technical issues, and maintaining system availability.
You will own the long-term architecture and technical direction for core data platform capabilities while ensuring reliability and cost efficiency. Additionally, you will design foundational platform components and lead cross-team technical initiatives to support engineering and data science teams.
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Interns will work within a multidisciplinary team to design and build chess experiences using software or data engineering principles. Responsibilities include building features, optimizing systems, developing ML models, and integrating AI applications.
The engineer will build and support Fund Accounting data solutions, including data integrations, transformations, and reconciliations. They will also develop data products to support investment operations and finance reporting for an asset management client.
The role involves building and maintaining data and analytics products while collaborating with cross-functional teams to drive engineering innovation. Responsibilities include designing ETL solutions, managing data ingestion, and supporting data scientists with scalable machine learning model deployment.
The agent will handle customer inquiries across multiple channels regarding automotive products, services, and technical issues. They are also responsible for resolving complaints, identifying customer needs, and updating internal databases.
The agent is responsible for providing timely, accurate, and empathetic assistance to customers across various channels. They handle inquiries, explain product features, resolve issues, and document solutions while utilizing AI-powered tools.
Build and modify high-complexity ETL and data pipelines while developing monitoring and alerting systems for infrastructure. Lead end-to-end data engineering initiatives and collaborate with cross-functional stakeholders to ensure data reliability and efficiency.
You will deliver customer projects across GenAI, machine learning, and broader AWS infrastructure, including landing zone deployments and migrations. You are responsible for building AI agents, training pipelines, and production infrastructure while ensuring technical documentation and testing standards are met.
The agent will handle customer inquiries via multiple channels regarding vehicle services, electromobility, and technical issues. They are also responsible for resolving complaints, updating internal databases, and assisting in the training of new team members.
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Design, develop, and maintain scalable data pipelines and ELT/ETL workflows using AWS, Databricks, and Spark. Collaborate with stakeholders to translate business requirements into high-quality data products and analytical solutions.
You will design, build, and maintain automation workflows for doctor onboarding and sign-up processes using tools like Make. Additionally, you will identify and implement AI-driven solutions to reduce manual tasks in customer service while analyzing conversion metrics to prioritize growth initiatives.
Design and implement scalable cloud-native data architectures using MS Fabric or Databricks for enterprise clients. Manage the full data lifecycle including ETL/ELT pipelines, data modeling, and performance optimization while collaborating with stakeholders.
Develop differentiated Gartner positions on the economic impact of AI and technology disruption across markets and business models. Partner with internal teams to synthesize quantitative evidence and domain expertise into actionable, market-shaping research for executive clients.
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