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Mia Labs is a venture-backed startup modernizing dealership operations through AI-powered agents. The company serves major dealership groups and is scaling rapidly across product, engineering, and go-to-market.
You will join the Data Platform team as a Senior Software Engineer, owning meaningful portions of the data infrastructure while contributing to core backend systems. This role blends data engineering depth with general backend Python work, emphasizing scalability, reliability, and AI-enabled workflows.
Key Responsibilities:
- Design, develop, and maintain scalable backend systems using Python for data services, pipelines, and scheduled jobs
- Design and ship data orchestration assets, jobs, and sensors using modern workflow tooling (e.g., Dagster)
- Develop and optimize data models across staging, dimensional, fact, and mart layers
- Improve data quality, observability, and reliability for downstream analytics and reporting
- Deliver dealer and group-level reporting foundations with reusable, well-tested metrics
- Build AI-enabled data workflows, including batch extraction pipelines, evaluation loops, and cost-aware LLM integration patterns
- Partner directly with product, finance, sales, and leadership to translate ambiguous asks into production-grade data products
- Contribute to broader backend engineering in Python/FastAPI as business priorities shift
- Lead system design and architecture discussions, ensuring technical scalability and maintainability
- Apply best practices in software development, including testing, CI/CD pipelines, and code quality
- Mentor and guide team members, fostering technical growth and collaboration
- Stay current with technology advancements relevant to the domain, including AI/ML, and evaluate their potential application
Requirements:
- 5+ 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
Nice to Haves:
- Experience with BI or analytics platforms such as Hex, Metabase, or Looker
- Experience with managed ELT ingestion tools such as Fivetran or Stitch
- Experience with identity resolution, entity linking, or customer 360/CDP systems
- Experience building customer-facing analytics or reporting products
- Familiarity with automotive data domains (dealerships, DMS/CRM, service operations)
