Data Engineer, Infrastructure

Aaru operates at the frontier of predictive intelligence, using AI to simulate and predict human behavior at scale. By generating and deploying instances of artificial intelligence that mirror humans, called agents, Aaru simulates entire populations with unprecedented accuracy. Our partners use Aaru to refine strategic positioning, identify and understand high-value audiences, validate concepts and messaging before launch, optimize pricing decisions, and build a continuously richer understanding of their customers through simulation. We provide organizations with invaluable foresight, empowering them to anticipate outcomes and proactively make the right decisions at the right time, every time.

We're a small, dedicated, mission-driven team and we intend to stay that way. We believe the best work happens when exceptionally talented people are given ownership, trust and the space to operate without bureaucratic friction. We work with urgency and intellectual honesty and expect new team members to match our velocity. We seek individuals who thrive at the frontier, who push beyond conventional limits, who bring curiosity and conviction in equal measure, and who want their work to have demonstrable impact in the world. If you're energized by the idea of a small team doing things that feel impossible, let’s build together.

As a Data Engineer on the Infrastructure team, you will build and maintain the data foundation that powers Aaru’s simulations. You will work across large internal and third-party datasets, designing reliable integration workflows, and ensuring that data can be linked, queried, and trusted at scale. This role sits at the intersection of data engineering and architecture and is critical to how Aaru produces predictive intelligence.

• 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 or incongruent datasets

• Work 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

• You have 3+ years of experience in data integration, data engineering, ETL/ELT, or a similar role involving large-scale datasets

• You have hands-on experience working with messy, high-volume data (>100 TB) and know how to build systems that remain reliable at scale

• You are highly fluent in SQL and Python, and comfortable working across modern data infrastructure such as Snowflake, BigQuery, Databricks, or similar tools

• You have strong judgment around data quality and know how to preemptively identify inconsistencies, edge cases, and integration risks

• Have experience with alternative data, (transaction data, clickstream, geospatial, etc) either from a hedge fund or data marketplace lens

• Have experience building matching or entity-resolution systems across fragmented or noisy identifiers

• Have familiarity with privacy, compliance, and data licensing considerations when working with sensitive or third-party data

• Have worked closely with researcher or product teams to turn raw data from disjoint sources into accessible structured database

• Have a background in statistics and familiarity with sampling biases, bot-detection, imputation, and standard data quality metrics

This role is based in New York City. Aaru is an in-person company, working 5 days a week in office. Candidates are expected to be located within the New York City metropolitan area or open to relocation.

At Aaru, we take care of our people. In addition to a competitive base salary and equity participation, we offer comprehensive medical, vision, and dental coverage, visa sponsorship and relocation support, and various other benefits and perks.