Data Platform Engineer

Your data sources have wheels. LionsBot designs and builds autonomous cleaning robots that work in the real world: malls, airports, offices and industrial sites across 30+ countries. More than 5,000 of them stream telemetry to us in real time: missions, maps, locations, incidents, battery health. We move fast: small team, quick decisions, zero bureaucracy, hardware you can kick. You'll be our first dedicated data hire, a true 0→1, greenfield ownership role. The foundations are in place: real-time telemetry streams from the fleet into a time-series store, with dashboards on top. Our fleet has now grown to the point where data deserves a full-time owner, so we're making it a first-class function. End-to-end, it's yours. The mission: take us from "a pipeline that works" to a streaming-first data platform with a proper medallion architecture: bronze raw telemetry, silver cleaned and conformed, gold business-ready marts. On top of it all, a semantic layer where every metric has exactly one definition and everyone trusts the number. The fun problems, all real • Robots report cumulative lifetime odometers on every mission row. Sum the wrong column and your fleet total inflates 1,000×. Design the models that make that mistake impossible. • A sensor glitch claims one robot cleaned 2.5 million m² in twenty minutes. Build the data quality and anomaly detection that catches it before a human ever sees it. • Robots in basements with bad Wi-Fi send late-arriving, out-of-order data. Make the pipelines idempotent anyway. • Real-time fleet health: which robots are sick right now, across 30+ countries and time zones? What you will do • Own the data platform end-to-end: ingestion, storage, modeling, serving, dashboards. Real-time event streams from the fleet land in a time-series database today. Where it goes next is your call. • Design the medallion architecture: bronze, silver and gold layers over high-volume IoT telemetry, with clear data contracts agreed with the backend teams so quality is designed in at the source. • Build the metrics/semantic layer: canonical, documented, version-controlled definitions for fleet KPIs: cleaning hours, area, mission success, incident rates, robot health. A genuine single source of truth. • Run data quality & observability like production software: freshness SLAs, validation, dedup, outlier handling, anomaly alerts. Flag the weird number before leadership does. • Design, tune and re-architect databases at scale: schemas, indexes, continuous aggregates, compression, downsampling, retention and partitioning, treating them like the production systems they are. • Make analytics self-serve: dashboards and models for ops, product, leadership and OEM partners, plus fast, rigorous answers to the high-stakes ad-hoc questions. • Shape the roadmap: we run lean today, so what comes next is genuinely open: OLAP, orchestration, transformation tooling, lakehouse patterns. You evaluate, make the case, and we adopt what earns its keep. • Work AI-native: we pair humans with LLM-powered analytics agents daily. You'll design the platform so both humans and AI agents can query it safely and correctly. What we are looking for • 3+ years working with data in production: data engineering, analytics engineering, or backend with heavy data exposure. We hire for trajectory, not year count. • Strong SQL, solid PostgreSQL and confident database design: schemas, indexes and data models that hold up as data grows. Time-series databases like TimescaleDB or InfluxDB are a big plus, but you'll learn them fast here. • Comfortable with event-driven data: you've worked with streaming or message-queue systems like Kafka, or you're a data-minded backend engineer keen to go deeper on real-time. • Solid Python for pipelines and tooling, and comfortable reading Go or Java services. • You've shipped dashboards and metrics people actually used, whatever the BI tool. • Fast and autonomous, like our robots: high ownership, pragmatic trade-offs, comfortable with ambiguity, ships iteratively. • Clear communication: you translate data into decisions, not just charts. Nice to have: • Production streaming chops: you know your at-least-once from your exactly-once. • IoT, robotics, or high-volume device telemetry experience. • AWS, especially EKS, RDS and S3, with exposure to Azure or GCP. • OLAP engines, orchestration or transformation tooling, CDC pipelines. • Search engines like Quickwit or Elasticsearch, or graph databases like Neo4j. • Geospatial data: our robots navigate real floors, so maps and location streams are first-class citizens. • Experience making data platforms LLM/agent-friendly: semantic layers, governed self-serve. • Familiarity with OpenRMF, ROS or robotics-related communication stacks Why join: • 0→1 ownership. The architecture, the standards, and the tooling choices are yours to shape. Eventually, so is the team. • Grow with the function. We're hiring for trajectory: as data grows from one person into a team, you're first in line to lead it. • Physical-world data at real scale. Thousands of robots, 30+ countries, real-time streams. Not clickstream. Not ad attribution. Robots. • Visible impact. Small team, direct line to leadership. What you ship this week is used in decisions next week. • AI-forward team. We already run AI-assisted analytics in production workflows. You'll multiply it, not fight it.