Software Data Engineer, Data Platform

Our mission is to transform how people and machines work together to push the boundaries of human productivity. A leader in Industrial AI, Augury helps the world’s manufacturers leverage real-time production insights to drive new levels of efficiency. Combining predictive and prescriptive AI technology with industry expertise, production teams can proactively address alerts, minimize downtime, reduce asset costs, and maximize yield and capacity. Our customers achieve payback in six months or less, enabling global scale. We're looking for team members excited to partner with the world's manufacturers and build the future of production together. You are a Software Data Engineer with deep experience building data-intensive systems, not a traditional ETL or BI-focused Data Engineer. In this role, you will design and build production-grade data services, platforms, and pipelines that power DIH and our AI-driven products. You will combine strong software engineering fundamentals with modern data engineering practices, with a focus on clean architecture, reliability, scalability, observability, and testing. As a Software Data Engineer, Data Platform, you will: Build and evolve Python-based services and pipelines that ingest raw industrial events, store them reliably, and expose clean, well-modeled tables and APIs for downstream consumers, including Digital Twin, Smart Canvas, AI agents, and analytics. Design systems that handle duplicates, invalid data, late-arriving events, and reprocessing in a principled, incremental, and reproducible manner. Collaborate with platform, machine learning, and product teams across Israel and globally to transform complex data challenges into robust, observable, and scalable software solutions. A Day in Your Life Production Data Systems & Pipelines Design and implement end-to-end data flows, from raw event ingestion into durable storage to modeled datasets and aggregates that power products, Digital Twin capabilities, analytics, and AI agents. Build idempotent pipelines that can safely re-run without corrupting data, using deterministic keys and clearly defined contracts between raw, curated, and modeled datasets. Implement incremental aggregations (e.g., machine signal summaries, production metrics, and operational KPIs) that correctly account for late-arriving data, watermarking strategies, and reproducibility requirements. Model relationships and context across machines, lines, factories, sensors, work orders, and operational events to support context-aware applications, knowledge graphs, and AI agents. Partner with platform teams to define how datasets are stored within our lakehouse, Digital Twin, and context graph architectures and exposed through well-defined APIs and tools. Software Engineering & Data Quality Write clean, maintainable Python services with clear separation of concerns across ingestion, validation, transformation, persistence, aggregation, and orchestration layers. Apply strong data modeling and SQL fundamentals, including schema design, indexing strategies, event-time semantics, and scalable aggregation patterns. Drive testing discipline across the data platform, including unit tests, data-quality tests, integration tests, and validation frameworks. Design for observability through metrics, logging, tracing, and monitoring that simplify debugging, improve data quality visibility, and support production operations. Troubleshoot and resolve production data issues, including incorrect aggregations, missing data, duplicate records, schema evolution challenges, and backfill operations. Streaming, Lakehouse & Scalability Build and evolve systems that scale from local development environments to cloud-scale lakehouse architectures using technologies such as Databricks, Delta Lake, and Spark. Design and implement data pipelines following modern lakehouse patterns, including Bronze, Silver, and Gold layers, partitioning strategies, and cost-efficient compute utilization. Work with strea