Job Description

Senior Analytics Engineer, Developer Productivity & DevSecOps

About the role

We are looking for an Senior Analytics Engineer to build and maintain the data pipelines, metrics, dashboards, and reports that measure the impact of our DevSecOps transformation program on the developer community.

This role combines data engineering, analytics, and reporting with an understanding of how software is developed and delivered. You will work with program leaders, development teams, platform teams, and data engineering teams to turn data from developer tools and enterprise systems into reliable insights.

Your work will help us understand tool adoption, guide migrations between platforms, identify delivery bottlenecks, and evaluate changes in developer productivity, software quality, and operational reliability. You will deliver rapid reporting prototypes, develop maintained reporting solutions, and serve as the curator of our metric definitions.

Key responsibilities

• Build dashboards and reports. Develop and maintain Power BI reporting across software delivery performance, developer activity, tool adoption, onboarding, quality, reliability, and AI tool usage.
• Deliver rapid prototypes. Respond quickly to emerging questions with practical Excel analyses, preliminary dashboards, and reports built from available data. Convert CSV and JSON extracts into usable reporting, validate initial findings, and refine requirements with stakeholders. Develop prototypes into maintained solutions where needed.
• Discover and understand data sources. Identify the systems, APIs, tables, and extracts needed to answer reporting questions. Understand how source data is generated, what it represents, and the limitations that affect its use.
• Build and coordinate data pipelines. Use Python, SQL, and appropriate platform capabilities to extract, transform, and load reporting data. Partner with Databricks and other data engineering teams when they own upstream pipelines, and help resolve data access, integration, and refresh issues.
• Develop consistent data models and calculations. Combine data across systems, reconcile developer identities, connect activity to organizational structures, and implement consistent reporting populations, time periods, and calculation logic.
• Measure adoption and migration. Establish tool usage baselines, track movement from existing platforms to replacements, and identify adoption gaps, inactive users, and opportunities to improve license utilization.
• Analyze development and delivery workflows. Engineer DORA and related metrics, including deployment frequency, lead time for changes, coding and review times, commit activity, pull request and merge request activity, and work progress captured in Jira.
• Evaluate AI adoption and outcomes. Report on access, engagement, and usage of AI development tools. Use appropriate baseline, trend, and group comparisons to assess relationships between adoption and development outcomes.
• Own and curate metric definitions. Maintain a shared metric dictionary covering each measure’s purpose, calculation, data sources, reporting population, time periods, exclusions, assumptions, and limitations. Coordinate agreement on definitions and manage changes to preserve consistency.
• Explain and interpret the metrics. Serve as the point of reference for what each metric means, how it is calculated, and how it should be used. Investigate questions about reported figures and explain findings clearly to technical and business audiences.
• Maintain reporting reliability. Validate accuracy, completeness, and freshness; investigate discrepancies; troubleshoot failed pipelines and report refreshes; maintain appropriate access controls; and document how reported measures trace back to their sources.

Required skills and experience

• Power BI: Practical experience building and maintaining dashboards, reports, and data models, including DAX, Power Query, scheduled refreshes, and row level security.
• SQL: Strong ability to write, troubleshoot, and optimize queries involving complex joins, aggregations, window functions, and time based calculations.
• Databricks: Experience querying and preparing data, working with Unity Catalog catalogs, schemas, tables, and views, and collaborating with teams responsible for data access and pipelines.
• Python: Practical experience extracting, cleaning, transforming, and validating data, integrating with APIs, and automating recurring data processes.
• Data integration and modeling: Experience building or maintaining ETL/ELT processes, combining data from multiple systems, and working with API authentication, CSV files, and JSON data.
• Excel: Proficiency in importing and transforming extracts, preparing and reconciling data, and building prototype analyses and reports using Power Query, formulas, and pivot tables.
• Software development lifecycle: Intermediate understanding of Git version control, commits, branching, pull requests, merge requests, code review, testing, CI/CD pipelines, and production deployments. Ability to connect these workflows to the data used to measure them.
• Analytical judgment: Ability to define meaningful measures, validate assumptions, distinguish activity and adoption from outcomes, and explain what the available evidence supports.
• Responsiveness and ownership: Ability to work with incomplete or evolving requirements, deliver useful initial analyses quickly, apply appropriate validation, and carry reporting work through delivery and ongoing maintenance.
• Communication and collaboration: Ability to translate stakeholder questions into data requirements, coordinate across teams, document definitions and decisions, and explain calculations and findings clearly.

Preferred skills and experience

• Familiarity with reporting data from Jira, GitLab, GitHub, GitHub Copilot, Databricks Assistant, ServiceNow, workforce systems, or learning management systems.
• Experience measuring developer productivity, DevSecOps transformation, software delivery performance, or technology adoption.
• Experience working with Excel, CSV, and other supplemental reporting inputs stored in SharePoint.
• Experience using AI coding assistants such as Codex to accelerate data manipulation, Python and SQL development, analysis, and reporting automation, with the ability to review and validate generated code and outputs.

What success looks like

Stakeholders receive timely, useful reporting supported by dependable data processes and clearly defined metrics. Emerging questions can be answered quickly through validated prototypes, and recurring needs become maintained reporting solutions. Reported figures can be traced to their sources, definitions are consistently understood, and insights help teams make informed decisions about the DevSecOps transformation program.