Data Engineer
Build and maintain automated ETL pipelines to integrate marketing platforms and CRMs into a central database. Develop and manage data dashboards while automating recurring business reports and analyzing performance metrics.
Remote Data Engineer Jobs in Israel
82 Data Engineer jobs in Israel available for remote work from home. Apply for positions such as Data Engineer, Data Engineer, Data Engineer and more! Discover the best work-from-home or hybrid, full- and part-time jobs.
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Build and maintain automated ETL pipelines to integrate marketing platforms and CRMs into a central database. Develop and manage data dashboards while automating recurring business reports and analyzing performance metrics.
The Data Engineer designs, builds, and maintains scalable data infrastructure and ETL pipelines to support digital marketing strategies. They ensure data integrity and quality while collaborating with cross-functional teams to prepare data for dashboards and reporting.
Design, build, and maintain scalable batch and streaming data pipelines, transforming data from diverse sources into governed, analytics-ready datasets and reusable data services. Collaborate with clients, analysts, data scientists, and architects to ensure data quality, security, governance, performance, and clear technical documentation.
The Data Engineer will design, build, and maintain scalable data pipelines and ETL/ELT processes to integrate multiple data sources. They will also ensure data quality and collaborate with cross-functional teams to support analytics and data-driven decision-making.
Design, develop, and optimize scalable data pipelines using Azure Data Factory and other cloud-native services. Implement secure data migration strategies, including encryption and masking, while ensuring reliable performance across Azure environments.
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You will design, develop, and maintain scalable data pipelines and ETL/ELT processes while modernizing legacy infrastructure. Additionally, you will collaborate with the analytics team to deliver effective data solutions and ensure the reliability and performance of the data platform.
The Data Engineer will design, optimize, and own data pipelines that scrape, process, and ingest transaction and listing data. They will also build monitoring systems to track data metrics and continuously improve the data infrastructure.
Design and operate a multi-source data platform for ingestion, event-driven processing, and orchestrated batch workflows. Develop robust data pipelines using tools like Airflow and dbt to ensure reliable, standardized company intelligence.
Design, build, and manage scalable multi-cloud data platforms while developing real-time and batch data pipelines. Ensure high standards of data reliability, security, and governance while mentoring engineering team members.
Design, build, and maintain data pipelines and analytics solutions using Databricks and Python. Collaborate with IT teams to implement data governance standards and support Power BI reporting.
Own and develop the company’s analytics infrastructure, including data collection, ETL/ELT pipelines, orchestration, backfills, monitoring, and integrations with marketing, billing, product, and internal data sources. Build and maintain dbt models, implement data quality controls, and document data sources, models, and business logic for analytics teams.
You will design and scale robust data architecture to support analytics, experimentation, and decision-making across the company. You will also lead data quality initiatives and collaborate with cross-functional teams to build scalable data systems.
Design and build a multi-source data platform for concurrent ingestion, event-driven processing, and orchestrated batch workflows. Manage the platform end-to-end, including architecture, ingestion, production operations, and data transformation using dbt.
Design, develop, and maintain batch and event-driven data pipelines while optimizing IBM DB2 database performance. Collaborate with cross-functional teams to integrate legacy systems with modern cloud platforms and ensure data reliability.
The Senior Data Engineer will design, develop, and maintain scalable ETL/ELT pipelines while managing AWS workflows and orchestration tools. Additionally, the role involves leading a small team, collaborating with cross-functional stakeholders, and implementing AI-driven automation for engineering tasks.
Identify and deliver measurable cloud cost savings across AWS and GCP, and build reliable billing and usage data pipelines, models, and marts. Develop cost attribution, forecasting, anomaly detection, reporting, and automated governance tools while partnering with Engineering, Finance, and Product.
You will architect and lead the development of large-scale data pipelines and streaming systems to process billions of daily events. Additionally, you will own production data systems, ensuring high uptime, reliability, and scalability while collaborating with cross-functional teams.
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Design, develop, and maintain scalable ETL/ELT data pipelines and cloud-native data solutions using Python, PySpark, and Snowflake. Collaborate with cross-functional teams to optimize data performance, ensure data quality, and implement robust data governance practices.
Design and build scalable data pipelines to support machine learning workflows, including feature engineering and real-time data ingestion. Collaborate with cross-functional teams to ensure data quality, monitoring, and efficient model deployment.
Design and build scalable data pipelines while optimizing transformation workflows in Snowflake or Databricks. Collaborate with cross-functional teams to ensure high-quality, performant, and secure data systems for analytics and reporting.
The roles involve designing, building, and maintaining scalable data pipelines, cloud infrastructure, and analytical dashboards to support business decision-making. Responsibilities also include ensuring data quality, automating infrastructure, and collaborating across technical and business teams.
You will build and operate the backend of a Web Analytics and AI portal, owning features from design to production. This includes managing APIs, data layers, authentication, and ensuring system reliability through observability and performance optimization.
Design, build, and optimize complex data models and graph structures within SAP HANA Cloud to support enterprise-level applications. Collaborate with machine learning and search engineering teams to ensure efficient data pipelines and high-performance query execution.
Lead complex data engineering projects, designing and maintaining scalable data architectures, pipelines, warehouses, and infrastructure while ensuring data quality, security, performance, and alignment with business outcomes. Collaborate across teams, recommend appropriate tools and technologies, and mentor engineers and client teams.
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.
You will own the design, delivery, and operation of complex data pipelines, datasets, and platform components. You will also collaborate with product and business teams to translate requirements into scalable data models and reliable technical solutions.
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Design, build, and maintain scalable data pipelines and cloud data solutions using Snowflake. Collaborate with engineering and analytics teams to ensure high-quality data availability for reporting and decision-making.
You will design and build new cost-management features, including forecasting, anomaly alerts, and data-visualization tools. Additionally, you will develop data pipelines and transformation models while ensuring the reliability and performance of the application.
Design and maintain scalable ETL/ELT pipelines and data workflows while ensuring data quality and security. Contribute to the development of voice-agent solutions, including speech-to-text, text-to-speech, and LLM integrations.
You will design, build, and optimize scalable data pipelines to integrate large volumes of data into the analytics platform. Additionally, you will collaborate with cross-functional teams to ensure data quality, governance, and system performance.
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