Collaborative Robotics

• Deliver exceptional deployment support and elevate the customer experience. • Lead onsite hardware and software testing to ensure optimal cobot performance. • Troubleshoot, document, and resolve issues to enhance system reliability, and user experience. • Perform parts replacements and robot retrofits to maintain high operational standards. • Build and maintain strong customer relationships grounded in trust, reliability and mutual success. • Prepare sites for deployment through readiness assessments, checklists, and testing. • Create clear operational and technical documentation to build a foundation for excellence. • Partner with Program Manager to drive consistent communication and project progress.

• Own the full ingestion path from edge to cloud, ensuring robot telemetry, sensor data, and warehouse events are reliably captured, transported, and made available for downstream systems. • Design, build, and operate scalable pipelines and foundational data layers (streaming and batch) that deliver low-latency, reliable data for analytics, AI/ML, and product features. • Build and maintain ingestion pipelines from object storage (e.g., S3) into Databricks, including raw → staged → analytics-ready layers, supporting both streaming and batch workloads. • Own the reliability and CI/CD of the data warehouse and foundational data layers, enabling safe, repeatable deployment of schema changes, transformations, and infrastructure that analytics engineers depend on. • Implement observability, monitoring, and data quality checks to ensure pipeline correctness, detect failures or drift, and maintain trust in data used by Vista, Portal, and Scoutmap. • Scale and optimize multi-tenant data infrastructure, balancing performance, reliability, and cost-efficiency as Cobot’s customer base and data volume grow. • Collaborate directly with robotics, AI/ML, product, and analytics teams to translate product requirements into resilient data systems that unlock customer-facing features. • Establish and enforce best practices for data engineering, reliability, security, and CI/CD across ingestion, staging, and warehouse layers—owning the foundations while enabling analytics engineers to ship metrics, marts, and dashboards efficiently.

• Assist in gathering data by interviewing clients to understand data availability, structure, and formatting. • Identify gaps or inconsistencies in datasets and coordinate requests for additional data as needed. • Collect, clean, and analyze datasets to ensure data integrity and readiness for modeling. • Design, build, and maintain data models to support active Flywheel client engagements. • Use tools such as Excel, Power BI, Tableau, SQL, Python, or similar platforms to analyze and visualize data. • Support the development of standardized models, tools, and resources to improve how Flywheel performs data analysis and modeling across engagements. • Develop clear documentation for models, assumptions, and methodologies. • Assist in training internal team members or client stakeholders on how to interpret and use analytical outputs. • Prepare presentations, charts, and summaries that translate technical analysis into clear insights for both technical and non-technical stakeholders. • Support project leads during client meetings by providing analytical context and answering data-related questions. • Support the analysis of client processes across physical, digital, operational, and data layers to identify use cases for robotic automation and AI-driven efficiencies. • Support the development of strategic roadmaps for client robotics transformation aligned with business goals. • Contribute to internal initiatives that improve delivery quality, repeatability, and efficiency. • Share insights and learnings informally with the team based on project experience. • Support business development efforts by assisting leadership and sales teams with data analysis, proposal inputs, and responses to client requests.