Who we are:
Glydways is reimagining what public transit can be. We believe that mobility is the gateway to opportunity—connecting people to housing, education, employment, commerce, and care. By making transportation more accessible, affordable, and sustainable, we empower communities to thrive and unlock economic and social prosperity.
Our mission is to revolutionize transit with a solution that delivers high capacity, exceptional user experiences, unmatched affordability, and minimal environmental impact.
Meet the team:
The Data Platform team, part of the Autonomy Software organization, is a collective of applied data scientists and data engineers responsible for building robust data pipelines to keep tabs on our robot software. As the Data Platform Engineering Lead, you'll set the technical direction of our rapidly scaling data stack, staying hands-on to build its most critical pieces while growing the team of engineers behind it.
Roles & Responsibilities:
- Own the technical roadmap for our data platform, balancing near-term delivery against long-term scalability.
- Develop analytics and data accessibility solutions for internal engineering teams as well as external stakeholders.
- Set a high bar of technical excellence for data quality, validation, governance, and observability across the data lifecycle.
- Mentor and grow a team of analytics engineers and data engineering by guiding technical decisions, reviewing code and designs, and supporting career development.
- Partner with engineering leadership on planning, prioritization, and headcount.
- Collaborate cross-functionally with both technical and non-technical customers to platform new data analytics workloads.
- Research, evaluate, and integrate cutting-edge big data technologies to enhance our platform capabilities and influence build-vs-buy decisions.
Knowledge, Skills and Abilities:
- Degree in Computer Science, Analytics, Engineering or a related field.
- Management experience building and leading engineering teams is a must.
- Extensive experience building and operating production data platforms, with a track record of technical ownership over major systems.
- Proficiency with big data technologies (e.g., Spark, Hadoop, Hive, dbt).
- Proficiency with workflow orchestration tools (e.g., Airflow, Argo Workflows).
- Proficiency with multi-language build systems (e.g., Bazel, CMake) and containerization technologies (e.g., Docker, Kubernetes).
- Proficiency with cloud platforms (e.g., AWS, Azure, GCP).
- Expertise in Python and Shell.
- Expertise in SQL and/or SQL-like query languages.
- Expertise in version control systems (e.g., Git).
- Expertise in configuration languages (e.g., YAML, CUE).