Data Scientist / Data Engineer

Quick Facts

• Role: Data Scientist / Data Engineer

Description

You will design, implement, and monitor delivery and operational metrics end-to-end, sourcing data from development tools and presenting results in business terms. The work includes building KPI dashboards in Power BI, analyzing bottlenecks, and measuring the impact of GenAI/LLM solutions and API-based LLM integrations. You will document metric definitions and methodologies and communicate insights to teams and management.

Responsibilities

• Assess current metrics and select new metrics aligned with organizational goals

• Implement metrics end-to-end: metric definition, data sources, automated collection, and dashboards

• Gather and join data from Jira, GitLab, GitHub, and Gerrit via API and SQL

• Compute and monitor DORA metrics, Lead Time for Changes, Cycle Time, and flow metrics

• Build and evolve KPI dashboards in Power BI

• Interpret results and identify process constraints

• Translate insights into business language (time-to-market, delay cost, operational risk, customer impact)

• Measure effects of GenAI tooling (adoption, productivity/quality impact, ROI of licenses)

• Support solutions integrating with LLMs

• Recommend improvements and measure their impact

• Document metric definitions, methodologies, and reports in Confluence

• Present findings to teams and management

Requirements

• Experience in data analytics, Platform Engineering, DevOps, or Agile/IT process areas

• Very good knowledge of Jira (JQL, workflows, status history, dashboards) and Confluence

• Practical knowledge of GitLab (pipelines, CI/CD, deployments, Merge Requests), GitHub (repositories, Pull Requests, GitHub Actions, API), and Gerrit (code review, patchsets, review times)

• Proficiency in SQL (JOIN, aggregations, CTE, window functions) and Power BI (data modeling, Power Query, DAX, dashboards)

• Knowledge of LLM/AI technologies: model behavior, capabilities and limitations (hallucinations, data security, costs), and basics of prompt engineering and RAG

• Experience integrating LLMs with applications/processes via APIs; practical familiarity with GitHub Copilot (incl. adoption metrics) and Microsoft Copilot

• Knowledge of NotebookLM and Google Vertex AI

• Knowledge of DORA metrics and understanding differences between Lead Time for Changes and Cycle Time, including ability to calculate them

• Ability to choose and implement metrics for organizational goals; analyze trends using medians/percentiles; combine delivery speed with stability

• Ability to map metrics to business value (delivery speed value, client risk, rework costs), define KPI goals and recommendations, and evaluate data quality before conclusions

Benefits

• Sports card co-financing

• Ongoing support from a consultant

• Employee referral program