Design, develop, and maintain scalable data pipelines and ETL processes to power critical business insights. Collaborate cross-functionally with data scientists and stakeholders to translate business needs into technical data solutions.
Remote Data Engineering Jobs (1,307)
Tags = data-engineering
Design and maintain scalable data quality frameworks to ensure the accuracy and consistency of enterprise data across multiple domains. Implement automated checks within ETL pipelines and validate Power BI reports using AI-assisted practices and AIOps observability.
Act as the bridge between client business needs and technical design by owning the requirements-to-design arc. This includes leading discovery sessions, writing SQL for data probing, and translating complex healthcare challenges into buildable technical specifications.
Architect and build scalable data systems, pipelines, and platform integrations to drive client growth and marketing outcomes. Lead technical strategy conversations with stakeholders and translate complex data architecture into business narratives.
Senior Data Engineer
Method, a GlobalLogic company
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Full Time
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3 Weeks Ago
Method, a GlobalLogic company
Design and optimize data pipelines and infrastructure while contributing to data modeling and governance across client engagements. Collaborate with multidisciplinary teams to align data solutions with digital product goals and support AI readiness assessments.
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Global Strategy Analyst - Customer Success (Remote Friendly)
Docplanner
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Full Time
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3 Weeks Ago
Docplanner
Design and implement a global, AI-first Customer Success function focusing on productivity, insights, and best practices. Lead data analytics for retention and growth while acting as a Technical Project Manager for strategic initiatives across multiple markets.
Design scalable and performant applications using MongoDB while guiding customers on architecture patterns and business value. Partner with sales teams to drive account success through technical discovery, demos, and proof of value.
Design and maintain scalable data pipelines and lakehouse architectures to process operational data. Collaborate with business stakeholders to translate requirements into clean, analytics-ready datasets for BI and machine learning.
Own the architecture and operational execution of the marketing technology ecosystem to drive scalable lifecycle campaigns. Act as the technical bridge between data engineering, product teams, and marketing to ensure seamless data flows and member experiences.
Design and implement a centralized data platform to create a Single Source of Truth for strategic company metrics. Collaborate with Product Engineering to evolve reporting capabilities and enable Business Intelligence integrations for clients.
Develop and improve machine learning and statistical models to drive core algorithms for sports betting products. Collaborate with data engineering and product teams to deploy models and optimize performance through rigorous experimentation.
Develop and improve machine learning and statistical models to drive core algorithms for sports betting products. Create contextualized soccer feature sets and collaborate with engineering teams to deploy models into production.
Design and optimize enterprise-scale data solutions and reusable workflows within Snowflake and Azure environments. Build scalable ETL pipelines and implement data governance and compliance policies to ensure data quality.
Design, build, and ship AI solutions to automate workflows and enhance the Polaris IQ intelligence layer. Manage and develop a team of Junior AI Strategists while partnering with cross-functional engineering and product teams.
Database Developer (REMOTE)
Koniag Government Services, LLC
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Full Time
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3 Weeks Ago
Koniag Government Services, LLC
The Database Developer is responsible for designing, building, and maintaining data systems and engineering pipelines across AWS and Azure environments. This includes implementing CI/CD practices, developing serverless functions, and optimizing data processing and monitoring.
Run and evolve the Kubernetes landscape across Amazon EKS and on-prem clusters to support backend and data engineering teams. Design infrastructure abstractions and automate deployments to enable seamless shipping and high observability.
Optimize sales performance by building analytical systems for prospect scoring, lead allocation, and territory alignment. Deliver actionable insights and performance reporting to sales executives to drive revenue growth.
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Design and optimize PySpark/EMR pipelines and manage a Hudi-based medallion lakehouse on AWS. Own the end-to-end data platform reliability, cloud cost efficiency, and the deployment of data-serving APIs.
Design, build, and maintain scalable data pipelines and ingestion processes to ensure data quality and reliability across enterprise platforms. Support the development of modern data architecture and automate processes to improve operational efficiency for internal and external stakeholders.
Design, develop, and deploy scalable data pipelines and ETL/ELT processes using the Dataiku DSS platform. Support a large-scale enterprise data lakehouse migration while collaborating with international business and technical stakeholders.
Build and maintain end-to-end data pipelines and create AI-ready analytical datasets. Collaborate with stakeholders to incorporate business logic and optimize pipelines for reliability and performance.
The QA Engineer is responsible for designing and implementing automated test scripts and ensuring the delivery of high-quality applications. They also manage data integrity and quality across internal data platforms and pipelines.
Provide L3 engineering support for Palantir Foundry and Foundation environments, focusing on resolving complex technical issues and software defects. Maintain production pipelines, platform workflows, and contribute to platform governance and stability.
![Software Mind is hiring for remote [C3T] Corporate Data Architect](/assets/2083550c-5c59-4fae-b76e-a23e4817e5f2.jpeg){kind=small}
Design and standardize corporate data architecture to ensure consistency across analytics, AI, and system integrations. Act as a bridge between business strategy and technical implementation by creating reference architectures and data models.
Design and develop a unified data infrastructure platform to consolidate analytics instrumentation across GitLab. Build and maintain backend systems using Golang and Kubernetes to provide visibility into customer usage patterns.
Developers are responsible for building and supporting high-performance data solutions, monitoring and responding to a growing network of tools, and tackling complex technical challenges within the Data Analytics team. This involves creating and maintaining data pipelines and ensuring the reliability of data infrastructure.
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Senior Data Engineer - Bees Personalization
AB InBev | Growth Group
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Full Time
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24 Weeks Ago
AB InBev | Growth Group
The role involves leading the development and maintenance of data services and solutions to support products, downstream services, or infrastructure tools used across BEES. This includes designing efficient data models and implementing architectural improvements to enhance performance, monitoring, and evolution of data products.
The role involves designing, developing, and rapidly deploying scalable AI solutions to transform data into strategic capabilities for national security challenges. Responsibilities include developing new AI methods, rapid prototyping of models, and taking full accountability for client problems from discovery through production deployment.
Principal Data Solutions Analyst
Liberty Mutual Insurance
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Full Time
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24 Weeks Ago
Liberty Mutual Insurance
The role involves developing data pipelines for customers across the organization, utilizing tools like R, Python, and Redshift to build necessary features for data scientists. Responsibilities also include identifying data quality issues, communicating risks to stakeholders, and automating business scenarios from data pipeline building to final prediction algorithms.
The role involves designing, building, and maintaining production-grade data pipelines using tools like Airflow and AWS services to power analytics, machine learning, and operational systems. Responsibilities also include owning data ingestion, managing data storage across various platforms, and partnering with cross-functional teams on data model design.
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