Senior SDLC & DevOps Metrics Analyst (GenAI)

Quick Facts

• Role: Senior SDLC & DevOps Metrics Analyst (GenAI)

• Focus: End-to-end SDLC/DevOps measurement, KPI dashboards, and evaluation of GenAI impact

Description

You will assess and select engineering metrics for SDLC efficiency, implement them end-to-end (definitions, data sources, automated collection, and visualization), and translate the results into business language (e.g., time-to-market and AI ROI). The role includes integrating and validating data from Jira and DevOps tools, calculating DORA and flow metrics, and monitoring trends using medians, percentiles, and KPI views. You will also measure GenAI adoption and real impact versus baseline and support solutions integrating LLMs.

Responsibilities

• Evaluate current metrics and define new organization-aligned metrics, implemented end-to-end

• Integrate, validate, and ensure consistency of data from Jira, GitLab, GitHub, and Gerrit using APIs and SQL

• Compute and monitor DORA, Lead Time for Changes, Cycle Time, and flow metrics using statistical baselines (medians/percentiles) and trends

• Design and build clear KPI dashboards in Power BI (delivery speed, stability, quality)

• Measure and analyze GenAI tool effects: adoption level, impact on speed/quality vs baseline, and ROI for AI licenses; support LLM integration solutions

• Interpret metrics, identify bottlenecks (waiting, delays, rework), and communicate business risk and customer impact

• Produce prioritized improvement recommendations, align KPI goals, present insights to teams and management, and document methodology in Confluence

Requirements

• 3–5 years of experience in data analytics, Platform Engineering, DevOps, or IT/agile process optimization (senior)

• Practical knowledge of DORA metrics and ability to distinguish and calculate Lead Time for Changes vs Cycle Time

• Advanced Jira skills (JQL, workflows, status history, dashboards) and experience with Confluence

• Practical knowledge of DevOps toolchain processes: GitLab (CI/CD, pipelines, merge requests), GitHub (pull requests, GitHub Actions, API), and Gerrit (patchsets, review times)

• Strong SQL (JOIN, aggregations, CTE, window functions) and Power BI experience (data modeling, Power Query, DAX)

• Practical knowledge of GenAI/LLMs: capabilities, costs, limitations (e.g., hallucinations, security), prompt engineering; basic RAG and LLM integration via API

• Experience working with AI tools in daily workflows (e.g., GitHub Copilot adoption metrics, Microsoft Copilot; knowledge tools such as NotebookLM and Google Vertex AI)

• Ability to interpret statistical results (medians/percentiles), assess data quality, and convert analysis into business recommendations

Benefits

• Work with an interdisciplinary team of technology enthusiasts

• Long-term project involvement

• Access to latest technologies and engineering standards

• Flexible work arrangements: hybrid or fully remote (team-dependent)

• Learning ecosystem: internal trainings, topic guilds, cloud trainings and certifications

• Online English/German learning and group classes

• Multisport and private medical care on preferential terms

• Sponsored sailing trips in the Caribbean/ Mediterranean/ Northern Sea