Fetcherr is an AI-driven company specializing in deep learning, algorithmic trading, and large-scale data solutions. Our core technology, the Large Market Model (LMM), enables accurate demand forecasting and real-time, data-driven decision-making. Originally focused on the airline industry, Fetcherr is expanding its AI solutions across additional industries.
We are seeking a Data Architect to define and develop the data architecture that powers our cloud-native platform, machine learning workflows, and decision-making processes. You will work closely with engineering, product, data science, and analytics teams to ensure our data platforms, models, and pipelines are scalable, reliable, and aligned with business needs. While product and engineering teams remain responsible for their own implementations, you will provide the architectural direction, standards, and cross-team guidance needed to keep our data ecosystem cohesive and future-ready.
Key Responsibilities
- Define and evolve the company’s data architecture to support scalability, reliability, governance, and long-term platform growth.
- Partner with engineering, data science, and analytics teams to ensure data pipelines, storage systems, and data models align with architectural standards and business goals.
- Establish best practices for data modeling, data integration, data quality, metadata management, and lifecycle design across teams.
- Design architectural frameworks that support machine learning use cases, including data ingestion, feature generation, training datasets, and model-serving dependencies.
- Participate in design reviews and provide guidance on data architecture patterns, trade-offs, and long-term maintainability.
- Help teams make sound decisions around data platforms, processing frameworks, orchestration, and storage technologies.
- Support agile squads early in the planning process to ensure data architecture considerations are incorporated from the start.
- Mentor technical leads and senior engineers on data architecture thinking and connect local decisions to broader technical strategy.
- Contribute to data governance and consistency by promoting secure, well-structured, and discoverable data systems.