Backend Engineer

TAAS Partners•San Francisco, CA

10d•$150,000 - $220,000

About The Position

We are looking for a Founding Backend Engineer to architect the nervous system of our AI Operator. Our core decision engine relies on a perfect understanding of reality to make high-stakes financial trades. Your job is to build the Context Layer which serves as the sensory system that determines how our system sees the world. You will own the infrastructure that fuses rigid financial data with messy and unstructured signals. You will architect the universal data model that standardizes chaotic inputs from ad platforms, MMPs, first-party data and our own internal actions into a rigid and queryable schema to ensure our automated trading never misses a beat.

Requirements

A Systems Thinker: You have 4+ years of experience building high-throughput backend systems in Python, Go or Java. You care more about system design and data integrity than specific frameworks.
High Agency: You dont need a ticket to know what to do. You see a problem, you diagnose it and you ship a fix. You view code as a tool to drive business outcomes.
Data Polyglot: You are comfortable moving between SQL, NoSQL and message queues. You know how to handle data that arrives in different shapes, sizes and speeds.
Intellectually Honest: You are willing to challenge assumptions. If the data doesn't look right, you dig in until you find the root cause.

Nice To Haves

Real-Time Systems: Experience building low-latency systems such as high-frequency trading platforms, gaming backends or real-time bidding exchanges.
Complex Scraping: Experience gathering data from difficult sources where APIs do not exist or are rate-limited.
Distributed Systems: Familiarity with event-driven architectures (Kafka, RabbitMQ) and managing concurrency in a high-volume environment.
Data Engineering at Scale: Experience managing ETL pipelines for terabyte-scale datasets using tools like Airflow, dbt or Spark.

Responsibilities

Standardize Data Schemas: Design and maintain the canonical data models that normalize conflicting metrics across disparate sources. You will map data from ad platforms like Meta and Google, MMPs like AppsFlyer, first-party data and our own internal actions and observers into a single unified language for our models.
Build Deep 1P Data Integrations: Construct pipelines to ingest high-fidelity signals directly from the source. You will fetch events, conversion values, inventory logic and customer lifetime value models directly from enterprise data warehouses like Snowflake, Databricks and Redshift.
Integrate Unstructured Data & LLMs: Build workflows that scrape and process multimodal data from ad libraries and social platforms. You will work with LLMs to structure this chaos including competitor creatives, market trends and visual signals into quantitative inputs.
Architect the Observer Framework: Build the logging and observation infrastructure that records every state change. You ensure that every internal decision and external market shift is captured in our standardized schema to create a perfect historical record for causal analysis.