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Job Overview
Location
Austin Office
Job Type
Full-time
Category
Data Engineer
Date Posted
March 21, 2026
Full Job Description
đź“‹ Description
- • As a Software Engineer on the Data Platform team at Mia Labs Inc., you will play a pivotal role in modernizing the automotive retail industry by building scalable, AI-powered data infrastructure that transforms how dealerships operate and engage with customers. Your work will directly impact customer satisfaction, operational efficiency, and long-term dealer relationships by enabling data-driven decision-making across product, finance, sales, and leadership teams.
- • You will design, develop, and maintain robust backend systems using Python, focusing on data services, pipelines, and scheduled jobs while ensuring high reliability, observability, and scalability. This includes shaping data orchestration with modern tools, optimizing data models across staging, dimensional, fact, and mart layers, and delivering production-grade metrics for dealer and group-level reporting. You will also build AI-enabled workflows involving LLM integration, batch extraction, and evaluation loops, and contribute to broader backend engineering in Python/FastAPI as business needs evolve.
- • You will join a fast-growing, investor-backed startup trusted by some of the nation’s largest dealership groups, where your contributions will directly influence product direction and technical strategy. The Data Platform team operates at the intersection of data engineering, AI, and backend systems, offering a unique opportunity to solve complex problems in a high-impact domain.
- • In this role, you will deepen your expertise in modern data stack technologies, AI/LLM integration, and scalable backend architecture while mentoring teammates and leading system design discussions. You will gain end-to-end ownership of data products—from ambiguous business requirements to production-grade solutions—and develop strong cross-functional collaboration skills working closely with product, finance, sales, and leadership.
🎯 Requirements
- • 4+ years of professional software engineering experience with strong Python fundamentals
- • Hands-on experience with modern data stack tools such as Dagster, dbt, and a cloud data warehouse (BigQuery, Snowflake, or similar)
- • Strong SQL and data modeling exposure (dimensional modeling, incremental patterns, data quality testing)
- • Experience operating in cloud environments (Azure, GCP, or AWS) with production-grade practices: CI/CD, observability, secrets management
- • Practical experience integrating AI/LLM tooling into real workflows — LLM provider APIs, prompt iteration, eval-minded development, or batch processing at scale
- • Ability to move between data engineering and general backend engineering with minimal friction
- • Strong communication and ownership; comfortable working directly with senior cross-functional stakeholders
- • Experience with API integrations
- • Demonstrated ability to mentor and inspire team members
- • Excellent problem-solving and communication skills
🏖️ Benefits
- • Equity participation through stock options
- • Fully paid health, vision & dental insurance for employees
- • Flexible PTO and hybrid schedule (3 days / week in downtown Austin)
- • Free lunches, parking stipend provided, team events, and a casual, but get things done culture
Skills & Technologies
See exactly how your profile matches this role — strengths, skill gaps, and what to do about them.
About Mia Labs Inc.
Mia Labs provides AI-driven computer-vision software that analyzes customer behavior and product interaction in physical retail environments. Its platform anonymously tracks shopper foot-traffic, dwell time, product engagement, and shelf attention using existing security cameras, converting video into real-time analytics dashboards. Retailers and brands use the data to optimize store layouts, planogram placement, staffing, and marketing campaigns with measurable impact on sales. The company targets grocers, convenience stores, and consumer-goods manufacturers seeking to bridge the gap between e-commerce-style analytics and brick-and-mortar operations.
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