Our team focuses on defining and scaling enterprise metrics that capture software delivery performance and developer experience. You will work with data from development pipelines, tooling adoption, and engineering activity signals to uncover trends and improvement opportunities. You will help operationalize data products and pipelines that support consistent reporting and repeatable analysis. You will contribute to best practices in developer productivity measurement and modern analytics engineering.

Job responsibilities 

• Partner with product, engineering, and stakeholder teams to translate developer productivity and technology efficiency goals into measurable frameworks.
• Analyze complex datasets (e.g., pipeline performance, developer activity signals, and GenAI tool usage) to identify trends, patterns, and improvement opportunities across the software development lifecycle.
• Develop models to quantify the productivity and delivery impact of GenAI coding assistants, CI/CD improvements, and developer experience initiatives.
• Design and run experiments to test hypotheses on tool adoption and workflow changes, and validate outcomes for accuracy and reliability.
• Build and maintain dashboards, reports, and lightweight web experiences that surface key efficiency metrics (e.g., cycle time, throughput, and delivery performance).
• Engineer scalable analytics pipelines that ensure reliable data flows from source systems to reporting and modeling layers.
• Create clear, decision-ready narratives and recommendations for technical and non-technical audiences, including senior leaders.
• Establish metric definitions, data quality checks, and documentation to support consistent interpretation and governance.
• Collaborate with Technology and Finance partners to support impact attribution and investment measurement for major engineering initiatives.
• Stay current on emerging practices in developer productivity measurement, GenAI-assisted development, and analytics engineering.

Required qualifications, capabilities, and skills 

• Bachelors degree in Data Science, Statistics, Computer Science, or a related field.
• 4+ years of experience in data science, analytics, or a related role.
• Demonstrated ability to define metrics and measurement frameworks in ambiguous or unstructured problem spaces.
• Proficiency in analytics and visualization using tools such as SQL, Python, and Tableau (or equivalent).
• Experience with modern data warehousing or lakehouse platforms (e.g., Snowflake, Databricks, Redshift).
• Strong foundation in statistical methods, machine learning, and data mining techniques.
• Proven ability to derive actionable insights from large, complex datasets.
• Strong written, verbal, and presentation skills, with the ability to communicate to technical and non-technical audiences.
• Ability to work effectively both independently and collaboratively in a fast-paced environment.

Preferred qualifications, capabilities, and skills 

• Masters degree in Data Science, Statistics, Computer Science, or a related field.
• Experience working in Agile environments and using Jira and Jira Align (or similar tools).
• Familiarity with analytics engineering and orchestration frameworks such as dbt and Airflow (or equivalent).
• Working knowledge of DevOps metrics and software development lifecycle measurement concepts.
• Familiarity with AI-assisted coding tools (e.g., GitHub Copilot or similar).
• Experience with interactive BI tools such as Looker or ThoughtSpot (or equivalent).
• Experience mentoring junior data scientists or contributing to a collaborative, knowledge-sharing culture.

 

Measure and improve software delivery efficiency using analytics and data science across CI/CD and GenAI tools.