• Data Ingestion: Design and implement scalable data pipelines for ingesting and processing telemetry, logs and sensor data from our fleet of robots and associated cloud software. • Multimodal Data Integration: Develop systems to handle complex multimodal logs, ensuring precise synchronization between video, audio, IMU, and joint-state data for downstream analysis and robot 'black box' reconstruction/playback. • Edge Data Agents: Contribute to the development of data ingestion agents running on-robot to manage high-volume data throughput in variable connectivity environments. • Data Optimization: Implement and optimize data formats (e.g., MCAP, Parquet) specifically tuned for the storage and rapid querying of rich multimodal robotics recordings. • Observability systems: Develop and maintain observability systems using tools like fluentbit, OpenTelemetry etc enabling real-time monitoring and analytics of fleet operations. • Distributed Processing: Use Spark, Kafka, or Flink to transform raw robot data into structured datasets ready for business intelligence and long-horizon fleet analytics. • Stakeholder Collaboration: Work closely with AI and Hardware teams to ensure data tools meet the needs of those training the next generation of humanoid models. • Cloud Platform: Build and optimize tools for internal stakeholders for business intelligence and long-horizon fleet analytics and forecasting using existing tools like AWS S3, Athena, Sagemaker etc.

• Own and evolve Keyloop’s People Data Platform and HR analytics dashboard (Python/Streamlit) • Design and maintain data architecture (pipelines, schemas, models) across SAP SuccessFactors and other HR systems • Manage API integrations and data access with core platforms and third-party vendors • Deliver insights across People Metrics, TA, L&D, and Engagement, partnering with functional leads • Own BAU reporting, driving accuracy, automation, and self-service adoption • Ensure GDPR and data compliance, including access controls and auditability • Build and scale AI-driven HR solutions (LLMs, agentic workflows, document processing, large-scale pipelines) • Develop production-ready AI use cases (e.g., attrition insights, skills inference, policy Q&A, candidate processing) and evaluate emerging tools • Act as HR AI SME, enabling adoption through training, playbooks, and self-service frameworks across HR and G&A • Partner with senior leadership, IT (AWS), and governance forums to shape the People Technology roadmap and responsible AI practices

• Assist in building and maintaining ETL/ELT data pipelines using Matillion or similar tools • Work with Snowflake to store, process, and analyze data • Write, optimize, and maintain SQL queries for large datasets • Perform data extraction, transformation, and loading from multiple sources (APIs, S3 files, databases) • Monitor and troubleshoot data workflows and pipelines • Support scheduling and automation of jobs using orchestration tools • Ensure data quality, consistency, and reliability • Collaborate with team members and stakeholders to understand data requirements • Maintain proper documentation for data processes and workflows • Work in a fast-paced, collaborative environment while demonstrating ownership, analytical thinking, and problem-solving skills • Continuously learn and adapt to new technologies, tools, and data engineering practices

• Assist in building and maintaining ETL/ELT data pipelines using Matillion or similar tools • Work with Snowflake to store, process, and analyze data • Write, optimize, and maintain SQL queries for large datasets • Perform data extraction, transformation, and loading from multiple sources (APIs, S3 files, databases) • Monitor and troubleshoot data workflows and pipelines • Support scheduling and automation of jobs using orchestration tools • Ensure data quality, consistency, and reliability • Collaborate with team members and stakeholders to understand data requirements • Maintain proper documentation for data processes and workflows • Work in a fast-paced, collaborative environment while demonstrating ownership, analytical thinking, and problem-solving skills • Continuously learn and adapt to new technologies, tools, and data engineering practices