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Aaru

Data Engineer

NYC$250k–$450kfulltimemidAdded 3 weeks ago

About this role

Aaru seeks a Data Integration Specialist to build and maintain scalable data pipelines that power AI-driven population simulations. You'll design workflows to ingest, clean, and integrate large multimodal datasets from diverse sources, ensuring data quality and usability across the organization.

What you'll do

  • Build and maintain scalable ETL pipelines to ingest, clean, and integrate large multimodal datasets
  • Own data ingestion from APIs, flat files, cloud storage, and data warehouses
  • Design workflows for entity resolution, deduplication, and schema harmonization across disparate datasets
  • Establish and monitor data quality checks, validation logic, and documentation
  • Collaborate with engineering, research, and deployment teams to ensure integrated data is simulation-ready
  • Evaluate new data sources and determine integration approaches with existing assets

What they're looking for

  • SQL and Python
  • Data integration and ETL/ELT processes
  • Modern data infrastructure (Snowflake, BigQuery, Databricks)
  • Large-scale dataset management (100+ TB)
  • Entity resolution and data matching
  • Data quality assessment and validation
  • API integration
  • Privacy and data compliance considerations

Benefits

  • Competitive base salary and equity participation
  • Comprehensive medical, vision, and dental coverage
  • Visa sponsorship and relocation support
  • 5-day in-person work culture in NYC
  • Ownership and trust-based work environment
  • Work on cutting-edge AI and predictive intelligence
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Aaru

Aaru builds AI-powered population simulation technology that models human behavior at scale using LLMs and classical methods. The company is hiring Simulation Engineers, Platform Engineers, Data Integration Specialists, and Infrastructure Engineers to develop the core systems, data pipelines, and infrastructure that power these simulations.

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Likely interview questions

  • Walk us through a recent project where you built or maintained a data pipeline at scale (>100TB). What were the biggest reliability challenges and how did you solve them?
  • Describe your experience with entity resolution and data linkage. How have you handled matching across fragmented or noisy identifiers in practice?
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