Engineering Data Analyst

Engineering Data AnalystWork Location: 645 Clyde Avenue, Mountain View, CA, USAWork Schedule: Fully onsiteLength of Assignment: 6 monthsEducation and years of Experience:1) Bachelors Degree or higher in an applicable field2) 3-5 years of experience in data analytics, business intelligence, or a related fieldTop Skills:Strong SQL skills with experience querying complex, multi-source datasetsProficiency in Python or R for data manipulation, analysis, and automationHands-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 engineeringintelligence 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 Responsibilities1. 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 teams2. 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 targets3. 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 efforts4. 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 calculations5. Stakeholder Communication• Deliver weekly/monthly reports to engineering leadership• Translate complex data findings into clear, actionable recommendations• Support quarterly business reviews with relevant engineering metricsQualifications (Required)3-5 years of experience in data analytics, business intelligence, or a related fieldStrong SQL skills with experience querying complex, multi-source datasetsProficiency in Python or R for data manipulation, analysis, and automationHands-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 environmentExcellent 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 timezonesBackground in ad tech, media, or high-growth technology companiesLocation & AvailabilityUS-based with ability to work Pacific timezone hoursAvailable for occasional overlap calls with Bangalore (morning) and Warsaw (afternoon) teamsFull-time availability (40 hours/week) for 6+ month engagementCulture FitOperational Excellence – Systematic approach to problem-solving; attention to detail and data accuracySelf-Direction – Proactively identifies gaps and opportunities without waiting to be askedGlobal Mindset – Comfortable collaborating asynchronously with distributed teams across timezonesClear Communication – Explains complex analysis simply; writes documentation others can followContinuous Improvement – Iterates on dashboards and processes based on user feedback