Data Engineer

Job Summary

As a
Data Engineer
at
CurvePoint
, you will design and maintain the data infrastructure that powers
Wi-AI Core
— our real-time wireless sensing platform that uses Wi-Fi signals to detect and characterize people, objects, and threats in real environments.

You will architect scalable AWS-based data systems that collect, synchronize, and transform multi-modal data streams (Wi-Fi CSI, video, telemetry) from hundreds of distributed sensing devices. Your work will ensure our machine learning and research teams have clean, versioned, and reliable datasets for training and continuous model improvement.

Duties and Responsibilities

Cloud & Data Infrastructure (AWS Stack)

• Design and maintain event-driven data pipelines using
  AWS Step Functions
  ,
  EventBridge
  ,
  Lambda
  , and
  ECS/Fargate
  .
• Develop scalable ingestion workflows to move device data (CSI, video, metadata) into
  S3
  with proper partitioning, validation, and schema management.
• Implement ETL and transformation jobs using
  AWS Glue
  or containerized Python pipelines.
• Integrate
  SageMaker Pipelines
  or
  Step Functions
  to automate model-training and evaluation workflows.

Data Modeling & Versioning

• Define and enforce consistent data schemas, validation logic, and metadata standards for Wi-AI sensor data.
• Manage dataset versioning and lineage using
  Glue Data Catalog
  ,
  DVC
  , or equivalent versioning frameworks.
• Build data summarization and indexing layers to efficiently query multi-terabyte datasets.

Data Quality & Observability

• Implement automated quality checks, anomaly detection, and completeness tracking across data streams.
• Build dashboards and metrics to monitor data ingestion, latency, and device health (e.g., via CloudWatch or Grafana).

Collaboration & Integration

• Work closely with ML engineers and scientists to design reproducible dataset interfaces and feature pipelines.
• Support field and RF teams by ensuring new devices, environments, and firmware revisions integrate seamlessly into the data ecosystem.
• Contribute to internal tools that make it easy to search, visualize, and retrieve relevant subsets of multi-modal data.

Performance & Scalability

• Optimize data transfer, caching, and compute scheduling for high-throughput processing jobs.
• Apply best practices in
  serverless architecture, cost optimization, and security
  (IAM, KMS, VPC design).

Must Haves

• Bachelor's or Master's degree in
  Computer Science, Data Engineering, or Electrical Engineering
  .
• 3+ years
  of experience designing and maintaining
  data pipelines or cloud infrastructure on AWS
  .
• Deep experience with AWS services including
  S3, Lambda, ECS/Fargate, Step Functions, Glue, CloudWatch, and IAM
  .
• Strong
  Python
  development skills, including experience with
  Pandas
  ,
  NumPy
  , or
  PySpark
  .
• Proficiency in
  SQL
  and experience with schema design for large-scale analytical datasets (Parquet, Arrow, HDF5, etc.).
• Understanding of
  event-driven architectures
  ,
  serverless workflows
  , and
  data quality validation
  .
• Familiarity with
  CI/CD
  and infrastructure-as-code tools (Terraform, CloudFormation, or CDK).

Nice to Haves

• Experience with
  multi-modal sensor data
  (e.g., video + time-series or IoT telemetry).
• Background in
  RF, signal processing, or embedded sensing systems
  .
• Familiarity with
  SageMaker
  ,
  MLflow
  , or
  Weights & Biases
  for model-training pipelines.
• Knowledge of
  data orchestration frameworks
  (Prefect, Dagster, or Airflow) for hybrid pipelines.
• Experience with
  monitoring and observability stacks
  (Grafana, Prometheus, or OpenTelemetry).

Location:
Pittsburgh, PA

(Hybrid role — occasional on-site collaboration at our research and test facilities.)