Role overview

Analog is developing robotics, spatial intelligence, and AI-driven experiences that operate in real-world environments. The company is building the core data platform that will power all of these products—handling ingestion, transformation, and storage of real-time data from deployed robots, live events, and spatial sensors. As a senior data engineer, you’ll be designing and constructing this foundational infrastructure from the ground up, rather than maintaining an existing system.

Key responsibilities

Design and build the core data infrastructure, including ingestion pipelines, data transformation layers, storage solutions, and connections between all components.
Make architectural decisions on how to handle data at scale, including schema design, storage tradeoffs, and ingestion patterns.
Build systems prioritizing reliability and performance from day one, knowing the platform will support live products serving real users, government entities, and enterprise partners.
Collaborate directly with product engineering teams across Hive, RaaS, and other product lines to understand data requirements and deliver solutions that meet their needs.
Own the operational aspects of the data platform, including cost management, performance optimization, and system reliability.

Requirements

Extensive backend engineering experience building and operating high-scale production systems, not limited to analytics or reporting pipelines.
Hands-on proficiency with distributed data processing frameworks such as Spark and workflow orchestration tools like Airflow.
Demonstrated experience designing data architecture from scratch, including schema design decisions, storage tradeoff analysis, and large-scale ingestion patterns.
Strong working knowledge of cloud data infrastructure on AWS or GCP, including operational concerns such as cost, performance, and reliability.
Ability to navigate ambiguity and make decisions in an early-stage platform environment without a predetermined playbook.
Experience with modern lakehouse tooling such as dbt or Iceberg, or familiarity with real-time and streaming data systems is advantageous.

What makes this role worth considering

Engineering at Analog operates as a flat organization without tech leads or middle management layers, meaning you have direct influence over architectural decisions and can implement improvements without navigating approval chains. The data you’ll work with is tangible and consequential—sourced from physical robots deployed in the field, live venues, and real-time athlete performance tracking—rather than abstract reporting systems.