Lead Data Engineer
On-siteSingapore, Singapore
Job Summary
Own the data platform end-to-end: ingest real-time telemetry from 5,000+ autonomous robots into a time-series store, then design a medallion architecture (bronze, silver, gold) with a semantic layer for single-source-of-truth metrics. Build data quality and anomaly detection to catch sensor glitches before they inflate fleet totals, ensuring pipelines handle late-arriving, out-of-order data idempotently. Create self-serve dashboards and models for ops, product, and leadership, while evaluating roadmap items like OLAP, orchestration, and LLM-powered analytics agents. Shape standards for high-volume IoT data across 30+ countries, treating databases and schemas as production systems.
Required Qualifications
- 3+ years working with data in production: data engineering, analytics engineering, or backend with heavy data exposure
- Strong SQL
- solid PostgreSQL
- confident database design: schemas, indexes and data models that hold up as data grows
- 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
- 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
Desired Qualifications
- Time-series databases like TimescaleDB or InfluxDB
- 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
- exposure to Azure or GCP
- OLAP engines, orchestration or transformation tooling, CDC pipelines
- Search engines like Quickwit or Elasticsearch
- graph databases like Neo4j
- Geospatial data
- maps and location streams
- Experience making data platforms LLM/agent-friendly: semantic layers, governed self-serve
- Familiarity with OpenRMF, ROS or robotics-related communication stacks
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