Data Platform Engineer
Posted Updated
Summary
First dedicated data hire at LionsBot, building a streaming-first data platform (medallion architecture, semantic layer, data quality) over real-time telemetry from 5,000+ autonomous cleaning robots. Core stack: SQL/PostgreSQL, time-series DBs, Python, streaming (Kafka), AWS.
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
*Only shortlisted candidate will be notified
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
*Only shortlisted candidate will be notified
