A no-code workflow engine for building, deploying, and validating production data pipelines across warehouses, ERP? systems, and ML? workflows.
Added May 24, 2026
Companies repeatedly need engineers to design, coordinate, and operate data pipelines that process large datasets, consolidate disparate systems, and support financial, analytics, and ML? workflows. These pipelines often require engineering best practices, orchestration tools like Airflow or Prefect, and validation systems to ensure reliable dataset delivery.
PipelineOps Workflow Builder gives technical and semi-technical teams a visual interface to define data sources, transformations, deployment schedules, and validation checks without hand-building every pipeline. It integrates with data warehouses and orchestration layers, generates production-ready workflows, and monitors throughput, failures, and dataset quality.
Job postings across data platform, ERP? transformation, analytics engineering, and ML? engineering roles show that production data pipelines are now a core operational need across many teams. The rise of ML?/LLM? workflows increases demand for repeatable, validated, high-throughput data movement.
Showing 1-20 of 22 signals
Designs and owns core Data Factory artifacts, including pipelines and dataflows, that support enterprise-scale data integration workloads with high throughput, fault tolerance, and performance. for a project or sub-section of a product and translates them into technical designs.
Build, maintain, and monitor operational workflows and automations used across the business. Develop and maintain data pipelines and system integrations to reduce manual work and improve operational efficiency.
and design, implement, and maintain scalable data pipelines in Palantir Foundry, ensuring end-to-end data integrity and optimized workflows. Design and maintain data ingestion, transformation, and orchestration pipelines using Palantir Foundry, Python, and PySpark.
Explore, clean, profile, and validate data from enterprise systems to improve model quality and reduce delivery risk. Design and implement prototypes, pipelines, model workflows, orchestration logic, and integration components.
* Lead project workstreams, ensuring timely delivery and alignment with platform roadmap; operate independently and drive outcomes end-to-end. * Build and optimize data pipelines for ingestion, transformation, storage, and retrieval supporting ML workflows (batch and streaming).
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