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Data Analyst 3 (Engineering Data Analyst)

Job ID: 26-00728
Pay rate range - $55/hr. to $60/hr.
Work Schedule: Fully onsite

Education and years of Experience:
1) Bachelor's Degree or higher in an applicable field
2) 3-5 years of experience in data analytics, business intelligence, or a related field

Top Skills:
• Strong SQL skills with experience querying complex, multi-source datasets
• Proficiency in Python or R for data manipulation, analysis, and automation
• Hands-on experience with BI/visualization tools (Tableau, Power BI, or similar)
• Familiarity with engineering workflows and tools (JIRA, Git, CI/CD concepts)

KEY RESPONSIBILITES/REQUIREMENTS:
We are seeking a Data Analyst contractor to support the Core Engineering organization. In this embedded role, you will partner directly with engineering leadership to build and maintain a comprehensive engineering
intelligence platform spanning delivery metrics, quality indicators, and team health analytics across our global development centers in the US, Bangalore, and Warsaw.
The ideal candidate combines strong technical skills (SQL, Python, Looker) with analytical rigor and clear communication. You will work across multiple data sources—including JIRA, HR systems, Git, and CI/CD pipelines—to surface actionable insights that drive operational decisions and team effectiveness.

Key Responsibilities
1. Engineering Metrics & Dashboards
• Design, build, and maintain dashboards for sprint velocity, cycle time, release frequency, and deployment success
• Create automated reporting pipelines using Python to reduce manual data gathering
• Establish standardized metrics definitions across US, Bangalore, and Warsaw teams

2. Quality Analytics
• Track and visualize bug rates, test coverage, incident response times, and technical debt trends
• Build early warning systems to identify quality issues before they impact delivery
• Partner with engineering leads to define quality benchmarks and improvement targets

3. Team Health & Capacity Planning
• Develop capacity planning models and utilization dashboards
• Analyze hiring pipeline data to support workforce planning decisions
• Monitor attrition patterns and provide insights to support retention efforts

4. Data Integration & Automation
• Connect and normalize data from JIRA, HR systems (Workday), Git repositories, and CI/CD tools
• Build reliable ETL processes to ensure data freshness and accuracy
• Document data sources, transformations, and metric calculations

5. Stakeholder Communication
• Deliver weekly/monthly reports to engineering leadership
• Translate complex data findings into clear, actionable recommendations
• Support quarterly business reviews with relevant engineering metrics

Qualifications (Required)
• 3-5 years of experience in data analytics, business intelligence, or a related field
• Strong SQL skills with experience querying complex, multi-source datasets
• Proficiency in Python or R for data manipulation, analysis, and automation
• Hands-on experience with BI/visualization tools (Tableau, Power BI, or similar)
• Familiarity with engineering workflows and tools (JIRA, Git, CI/CD concepts)
• Ability to work independently and manage multiple priorities in a fast-paced environment
• Excellent communication skills—can translate data into clear insights for technical and non-technical audiences

(Preferred)
• Experience with Looker (LookML knowledge a plus)
• Experience with engineering metrics (velocity, cycle time, DORA metrics)
• Exposure to HR/people analytics (capacity planning, attrition analysis)
• Familiarity with data pipeline tools (dbt, Airflow, or similar)
• Experience working with distributed/global teams across multiple timezones
• Background in ad tech, media, or high-growth technology companies


• Operational Excellence – Systematic approach to problem-solving; attention to detail and data accuracy
• Self-Direction – Proactively identifies gaps and opportunities without waiting to be asked
• Global Mindset – Comfortable collaborating asynchronously with distributed teams across timezones
• Clear Communication – Explains complex analysis simply; writes documentation others can follow
• Continuous Improvement – Iterates on dashboards and processes based on user feedback
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