Mia is the AI communication platform for car dealerships. We answer and act on every inbound call, text and web lead so dealers stop losing service appointments and sales to voicemail. Franchise dealers and some of the largest dealer groups in the country run on Mia.
ABOUT THE ROLE
We're hiring a Senior Software Engineer to join our Data Platform team. This role blends backend Python engineering with real data platform depth. You'll own our OEM billing pipelines, dbt models and dashboards, and data alerting, and you'll pick up backend work in our core systems as priorities shift. Much of the billing work is integration work: partner APIs, CRM data, and reconciling outputs where precision matters. This is a high-ownership role for someone who is comfortable working with AI day to day.
WHAT YOU'LL DO
Own OEM billing end-to-end: build and maintain integrations with partner APIs, HubSpot, and file-based data feeds, and reconcile outputs so every number is correct.
Build, maintain, and debug dbt models and dashboards as business needs come up.
Design and improve alerting across the data platform so issues surface before they reach customers or finance.
Drive data platform improvements: reliability, data quality, and tooling that makes the rest of the team faster.
Contribute to our backend python systems when priorities shift.
Chase down edge cases and discrepancies on your own. Billing correctness is part of your job.
WHAT WE'RE LOOKING FOR
5+ years of professional software engineering experience with strong Python fundamentals. You've shipped and owned production backend services or integrations, not only notebooks or SQL.
Strong SQL and data modeling: you can read a dbt DAG, fix a broken model, and reason about grain and joins.
Experience integrating with external APIs and files, including retries, idempotency, and reconciliation when the two sides disagree.
High ownership and attention to detail. You follow issues through to resolution without being asked.
Use AI to code (Claude Code, Cursor, Copilot, or equivalent). This is how we work, and you have informed opinions on the pros and cons of agentic workflows.
Based in Austin and able to work from our downtown office.
NICE TO HAVE
dbt in production plus an orchestrator (Dagster preferred; Airflow or Prefect experience transfers).
Cloud data warehouse experience (BigQuery preferred; Snowflake or Redshift transfers).
Billing, invoicing, revenue ops, or partner data-feed experience.
FastAPI or async Python web services.
CRM API experience (HubSpot or Salesforce).
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.
Compensation for this position will be competitive with current market rates and reflective of the candidate's experience, skills and qualifications.
At Mia, we’re committed to a workplace where everyone is valued, included, and empowered to do their best work. Our strength comes from diverse experiences, perspectives, and skills, and we’re proud to be an equal opportunity employer.
Passionate about the role but don’t tick every box? We’d still love to hear from you, you might be exactly who we need.
Mia is an EVerify employer.
Build and optimize a high-throughput ad exchange by improving its real-time bidder and throttling layer, analyzing billions of events, and translating interpretable machine-learning insights into live traffic rules.
Design and deploy secure distributed systems for AI-powered evidence search and analysis, bringing production machine learning, retrieval, and semantic reasoning capabilities from discovery through deployment.
Build and maintain backend product features, APIs, integrations, and background jobs while helping operate reliable cloud infrastructure, improve production systems, and guide teammates through reviews and mentoring.
Build and scale batch data platform infrastructure used by internal data engineers and scientists, automating Spark and JVM migrations, improving execution reliability, and optimizing resource allocation across large-scale workloads.
Build and own scalable, resilient backend microservices and customer-facing features across marketplace products, collaborating with cross-functional Agile teams to improve service quality, reliability, and engineering practices.
