Data Integration Engineer for Large-Scale AI Pipelines

Aaru; New York (NY); On-site; USD 110,000 - 150,000; Full time

Benefits offered by this job

Equity participation

Medical, vision, and dental coverage

Visa sponsorship and relocation

Job summary

Aaru is seeking a Data Integration Specialist to build and maintain the data foundation powering our simulations. You will work across large internal and third-party datasets, designing reliable integration workflows, and ensuring data can be linked, queried, and trusted at scale.

This role sits at the intersection of data engineering and architecture, critical to producing predictive intelligence and enabling accurate simulations for strategic decision-making.

Qualifications

• 3+ years of experience in data integration, data engineering, ETL/ELT, or a similar role involving large-scale datasets.
• Hands‑on experience working with messy, high-volume data (>100 TB) and building reliable, scalable systems.
• Fluent in SQL and Python; familiar with modern data infrastructure like Snowflake, BigQuery, Databricks.

Responsibilities

• Build and maintain scalable pipelines to ingest, clean, and integrate large multimodal datasets
• Own data ingestion across APIs, flat files, cloud storage, and data warehouses
• Design workflows for linkage, entity resolution, deduplication, and schema harmonization across imperfect datasets
• Collaborate with engineering, research, and deployment teams to make integrated data usable for simulation ingestion
• Establish and monitor data quality checks, validation logic, and documentation across datasets and pipelines
• Help evaluate new data sources and determine how they can be joined with existing data assets

Skills

SQL

Python

Data integration

Tools

Snowflake

BigQuery

Databricks

Job description

Aaru is seeking a Data Integration Specialist to build and maintain the data foundation powering our simulations. You will work across large internal and third-party datasets, designing reliable integration workflows, and ensuring data can be linked, queried, and trusted at scale.

This role sits at the intersection of data engineering and architecture, critical to producing predictive intelligence and enabling accurate simulations for strategic decision-making.