Data Engineer (AI)

TL;DR

Data Engineer (AI): Building and optimizing the data infrastructure that powers internal engineering workflows for radar and sensing systems with an accent on scalable data pipelines and PostgreSQL performance. Focus on designing robust systems for ingesting, organizing, and analyzing massive amounts of real-world sensor data to enable AI algorithm development.

Location: Hybrid in Burlington, MA. Must be legally authorized to work in the US without sponsorship.

Compensation: $145,000–$180,000

Company

MatrixSpace develops AI-enabled radar and sensing systems that provide situational awareness in challenging environments.

What you will do

• Design and build the internal data platform for ingesting and organizing field-collected radar data.
• Develop end-to-end data workflows including ingestion, transformation, enrichment, and aggregation.
• Optimize PostgreSQL databases through advanced schema design, indexing, and query tuning.
• Implement analytics and statistical processing across large-scale datasets.
• Ensure system observability, pipeline reliability, and high data quality.
• Collaborate with engineering, product, and infrastructure teams to ensure data accessibility.

Requirements

• 4-6 years of experience in Data Engineering or Backend Engineering.
• Strong programming proficiency in Python, C++, or Golang.
• Deep expertise in PostgreSQL performance tuning and schema design.
• Experience building real-time and batch data pipelines in production.
• Must be legally authorized to work in the US without employer sponsorship.
• Ability to obtain and maintain a U.S. government security clearance.

Nice to have

• Experience with streaming technologies like Kafka, Redpanda, or RabbitMQ.
• Familiarity with cloud-based data platforms (AWS) and modern data lake architectures (S3, Parquet, Delta Lake).
• Experience with orchestration tools such as Airflow or Dagster.
• Knowledge of distributed systems, sharding, and high-availability database design.

Culture & Benefits

• Opportunity to work on cutting-edge AI-enabled radar and sensing technology.
• Focus on long-term platform architecture rather than short-term fixes.
• Collaborative environment working across engineering, product, and AI teams.
• Ownership of systems from initial design through production operations.