About the role
Software Data Engineer, Data Platform | Augury 500+ manufacturers on AI, downtime, and what’s getting in the way. Download the report Solutions Industrial AI Workforce Combines machine and operational data to give manufacturing teams the monitoring, intelligence, and AI agents they need to accelerate response time and keep production running. Learn more Machine Health Prevent machine failures and reduce maintenance costs with AI powered by reliability experts. Machine Health Machine Health Solutions Critical Equipment Hazardous Ultra-Low RPM Supporting Equipment Auguscope Use Cases By Industry By Business Goal By Persona By Asset By Industry Food & Beverage Consumer Packaged Goods Chemicals Building Materials & Cement Metals & Mining Pulp & Paper Glass Pharmaceutical Plastics Steel Forest Products Oil & Gas Energy Commercial Services Show more By Business Goal Eliminate Unnecessary Downtime Reduce Loss, Waste, and Emissions Maximize Yield and Capacity Optimize Asset Care Transform the Way You Work Show more By Persona Plant Manager IT Maintenance & Reliability VP Manufacturing & Engineering Digital Transformation Supply Chain Process Engineering & Operations Show more By Asset Rotating Equipment Pumps Motors Compressors Bearings Fans and Blowers Gearboxes Chillers Conveyor Extruders Mills Mixers Seamer Show more Why Augury Customers Our Customers Our Commitment Success Stories Spotlight Awards Show more Partners Resources Resource Center Blog Events & Webinars Podcasts Videos Collateral Value Calculator Show more Company About Careers Media Center Support Community In the News Press Releases Show more Pricing Contact us Search for: Log in Book a Demo All Jobs R&D Software Data Engineer, Data Platform Our mission is to provide manufacturers and other industrial sectors with insights into the health of machines, processes, and operations to transform how people work and what they can create. A leader in Machine Health and Process Health solutions, Augury uses purpose-built AI technology, trained by industry experts and the world’s largest data library, to help companies realize the full potential of their production. Together with our customers, we are pioneering Production Health by removing friction created by competing business goals so companies can improve business outcomes, empower their workforce, and achieve sustainable production- all at the same time. Full-time About The Position Our Data Intelligence Hub (DIH) is building the next generation Industrial Data Intelligence platform: a contextual layer that connects machine health, operational, maintenance, engineering, and enterprise data on top of a site Digital Twin backbone. We use this foundation to power agentic, AI-native experiences that help users explore their sites, answer complex questions, and make better decisions in one place. You will be a core member of DIH, building production-grade data services and pipelines that power our Digital Twin, products, analytics, and AI agents. This is not a traditional ETL or BI-focused Data Engineering role. We are looking for an engineer with experience building data-intensive software systems, with a strong emphasis on clean architecture, reliability, scalability, and testing. Working closely with peers across India, Israel, and other global locations, you will help transform industrial and operational data into trusted, scalable, and actionable context for users, applications, and AI systems. A Day In Your Life Production Data Systems & Pipelines Design and implement end-to-end data flows, from raw event ingestion through durable storage and modeled datasets that power products, Digital Twin experiences, and AI agents. Build reliable, incremental pipelines that support deduplication, late-arriving data, watermarking, reprocessing, and reproducible aggregations at scale. Model context and relationships across machines, lines, factories, sensors, work orders, and tenants to support structured queries and AI-driven experiences. Partner with platform and AI teams to define how datasets are stored, modeled, and exposed through APIs, Digital Twin services, and context graphs. Software Engineering & Quality Build clean, maintainable Python services with strong separation of concerns across validation, persistence, aggregation, and orchestration layers. Apply strong SQL and data modeling practices, including schema design, indexing, constraints, timestamp semantics, and scalable aggregations. Drive engineering quality through automated testing, including unit, integration, and data-focused validation for correctness and reliability. Design for observability through metrics, logging, and tracing that support debugging, data quality monitoring, production incidents, and backfills. Streaming, Lakehouse & Scalability Design and evolve streaming-first architectures using lakehouse and messaging technologies, including partitioning, watermarking, replay, reprocessing, and cost-aware scaling. Work with technologies such as Kafka, Pub/Sub, or similar systems to build reliable event-driven services and data pipelines. Contribute to multi-tenant architectures and data contracts that enable secure, scalable access to data across products, applications, and AI agents. Collaboration & AI-Native Experiences Partner closely with DIH, Smart Canvas, AI, and Product teams to design scalable data models, APIs, and context services that power AI-native experiences. Translate business and product