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[job_card.job_description]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 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 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 Deliver weekly / monthly reports to engineering leadership Translate complex data findings into clear, actionable recommendations Support quarterly business reviews with relevant engineering metrics 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 Flexible availability for overseas calls 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 time zones 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 time zones Clear Communication – Explains complex analysis simply; writes documentation others can follow Continuous Improvement – Iterates on dashboards and processes based on user feedback
Job Title : Engineering Data Analyst
Location : Mountain View, CA
Pay Rate Range : style="color : #0e101a; margin-top : 0pt; margin-bottom : 0pt;">
Staffmark Workforce Solutions offers this exciting contract opportunity with a global leader in electronics, mobile devices, and appliances located in Mountain View, CA
Engineering Data Analyst
Top Skills
1. 3-5 years of experience in data analytics, business intelligence, or a related field
2. Strong SQL skills with experience querying complex, multi-source datasets
3. Familiarity with engineering workflows and tools (JIRA, Git, CI / CD concepts)
Position Description
We are seeking an Engineering Data Analyst 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.
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 the US, Bangalore, and Warsaw teams
2. Quality Analytics
3. Team Health & Capacity Planning
4. Data Integration & Automation
5. Stakeholder Communication
Requirements :
(Preferred)
Staffmark talent working with this client receive competitive compensation and a great benefits package including medical, dental, vision, 401K, and Paid Time Off, plus more!
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