Senior Data Engineer | Embodied AI
Posted
Summary
Senior Data Engineer joining the founding team of a US-based embodied AI company (hired via Dada Consultants in Singapore). Day to day: build and operate a petabyte-scale AWS data lake/lakehouse for multimodal spatial data — ingest pipelines, distributed CPU/GPU processing, open table formats, training-data delivery, plus on-call reliability and cost control.
We are partnering with a US-based embodied AI company to hire a
Senior Data Engineer . This is a hands-on, founding-team role suited to someone who has personally built and operated petabyte-scale data platforms in production. Job Responsibilities
Build and scale a PB-scale data lake / lakehouse for multimodal spatial data — object storage layout, partitioning, open table formats, catalogs, and metadata Own data versioning and lineage: branches, commits, reproducible dataset snapshots, and provenance from raw capture to customer delivery Build high-throughput, fault-tolerant ingest pipelines — upload, validation, checksumming, content-addressed deduplication, and promotion through bronze → silver → gold Build and operate large-scale distributed processing across CPU and GPU fleets — video decode, frame extraction, sync and calibration, large-scale 3D and spatial data processing, and model-based auto-labeling Build and maintain data pipelines at scale: idempotent jobs, retries, backfills, partitioned reprocessing, and clean failure recovery Build the dataset curation and query layer — metadata indexing, SQL over the lake, and search so researchers and customers can slice data without copying it Keep GPUs fed: large-scale ML training data formats, caching, prefetching, and efficient cross-region data movement Own platform reliability, observability, and cost on AWS — S3 tiering, lifecycle, egress, spot and right-sizing, capacity planning, and alerting Contribute to infrastructure-as-code and CI/CD; write design docs and runbooks so others can operate the platform without tribal knowledge Work closely with data capture, labeling, and QA teams to translate workload requirements into platform capabilities Job Requirements
B.S. or M.S. in Computer Science, Software Engineering, or a related field, or equivalent practical experience 6+ years in data engineering, data platform, or distributed systems engineering Hands-on experience building and operating a data platform at petabyte scale in production — able to speak concretely to volumes, throughput, object counts, and failure modes personally handled Deep object storage experience (S3 or equivalent): key/prefix design, throughput limits, small-file problem, lifecycle tiering, and cost control Production experience with open table formats — Iceberg, Delta Lake, or Hudi; catalogs such as Glue, Nessie, or similar Strong experience with distributed processing frameworks — Spark, Ray, Flink, or Dask — tuned for large production jobs Experience with workflow orchestration (Airflow, Argo, Prefect, or similar) with strong discipline around idempotency, retries, and data contracts Strong Python plus at least one of Go, Rust, C++, Java, or Scala Solid AWS infrastructure skills — EC2, EKS/ECS, S3, IAM, VPC — and Terraform or equivalent IaC Production on-call experience for a data platform; care deeply about uptime, correctness, and cost Licence Number: 18S9037 EA Registration Number: R23112003 Business Registration Number: 201735941W
Senior Data Engineer . This is a hands-on, founding-team role suited to someone who has personally built and operated petabyte-scale data platforms in production. Job Responsibilities
Build and scale a PB-scale data lake / lakehouse for multimodal spatial data — object storage layout, partitioning, open table formats, catalogs, and metadata Own data versioning and lineage: branches, commits, reproducible dataset snapshots, and provenance from raw capture to customer delivery Build high-throughput, fault-tolerant ingest pipelines — upload, validation, checksumming, content-addressed deduplication, and promotion through bronze → silver → gold Build and operate large-scale distributed processing across CPU and GPU fleets — video decode, frame extraction, sync and calibration, large-scale 3D and spatial data processing, and model-based auto-labeling Build and maintain data pipelines at scale: idempotent jobs, retries, backfills, partitioned reprocessing, and clean failure recovery Build the dataset curation and query layer — metadata indexing, SQL over the lake, and search so researchers and customers can slice data without copying it Keep GPUs fed: large-scale ML training data formats, caching, prefetching, and efficient cross-region data movement Own platform reliability, observability, and cost on AWS — S3 tiering, lifecycle, egress, spot and right-sizing, capacity planning, and alerting Contribute to infrastructure-as-code and CI/CD; write design docs and runbooks so others can operate the platform without tribal knowledge Work closely with data capture, labeling, and QA teams to translate workload requirements into platform capabilities Job Requirements
B.S. or M.S. in Computer Science, Software Engineering, or a related field, or equivalent practical experience 6+ years in data engineering, data platform, or distributed systems engineering Hands-on experience building and operating a data platform at petabyte scale in production — able to speak concretely to volumes, throughput, object counts, and failure modes personally handled Deep object storage experience (S3 or equivalent): key/prefix design, throughput limits, small-file problem, lifecycle tiering, and cost control Production experience with open table formats — Iceberg, Delta Lake, or Hudi; catalogs such as Glue, Nessie, or similar Strong experience with distributed processing frameworks — Spark, Ray, Flink, or Dask — tuned for large production jobs Experience with workflow orchestration (Airflow, Argo, Prefect, or similar) with strong discipline around idempotency, retries, and data contracts Strong Python plus at least one of Go, Rust, C++, Java, or Scala Solid AWS infrastructure skills — EC2, EKS/ECS, S3, IAM, VPC — and Terraform or equivalent IaC Production on-call experience for a data platform; care deeply about uptime, correctness, and cost Licence Number: 18S9037 EA Registration Number: R23112003 Business Registration Number: 201735941W