Engineering Data Analyst

Engineering Data Analyst

Work Location: 645 Clyde Avenue, Mountain View, CA, USA

Work Schedule: Fully onsite

Length of Assignment: 6 months

Education and years of Experience:

1) Bachelors 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

Location & Availability

• US-based with ability to work Pacific timezone hours
• Available for occasional overlap calls with Bangalore (morning) and Warsaw (afternoon) teams
• Full-time availability (40 hours/week) for 6 month engagement
• Culture Fit
• 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