Software Engineer II - Business Intelligence

Location: Ann Arbor, Michigan; remote within the United States

Salary: $139,000–$166,800 USD per year

Company

hirify.global develops autonomous vehicle technology and data systems for safety-critical automotive applications.

What you will do

• Design, build, and maintain ETL/ELT pipelines supporting the data architecture.
• Structure and optimize tables and views for reliable reporting and dashboard performance.
• Build data quality checks, validation processes, and documentation for leadership and board-level reporting.
• Partner with BI analysts and business stakeholders to translate reporting needs into data models and pipelines.
• Support enterprise SPI dashboards and consolidated TES/WBR metrics reporting.
• Migrate AWS/Athena-based data delivery to Databricks and define business KPIs using SQL and Python.

Requirements

• Bachelor's degree in Computer Science, Data Engineering, or a related field, plus 4+ years of professional data engineering or software engineering experience; equivalent experience is considered.
• Hands-on experience building ETL pipelines, data quality checks, and reporting-oriented tables and views.
• Proficiency in Python, PySpark, or similar tools, with experience using pandas, NumPy, or comparable data analysis tooling.
• Strong SQL skills and an understanding of downstream data consumption.
• Experience with BI tools such as Tableau, Power BI, or QuickSight.
• Remote candidates must be based in the United States.

Nice to have

• Databricks experience, especially with data serving, visualizations, and jobs.
• AWS experience with Athena, Glue, or other managed data services.
• Knowledge of relational, NoSQL, vector, and data warehouse architectures.
• Dashboard-building experience or interest in developing dashboards.
• Experience scaling data for ML/AI workloads, such as Ray, or experience in automotive, autonomous vehicles, robotics, or other safety-critical environments.

Culture & Benefits

• Work closely with BI analysts and stakeholders across the business.
• Help define data engineering and BI architecture, tooling, and reporting practices.
• Work on leadership-critical dashboards and metrics used for business decisions.