Senior Data Engineer

<p><b>•Data Analysis & Insights: Conduct deep-dive analyses across large datasets to identify trends, anomalies, patterns, and opportunities related to product performance, user behavior, adoption, and operational efficiency.</b></p><p><b>•Dashboarding & Reporting: Design, develop, and maintain scalable dashboards and automated reporting solutions using Plx/PLX dashboards, Looker, Google Data Studio/Looker Studio, or similar BI tools.</b></p><p><b>•KPI & Metrics Development: Partner with business and product stakeholders to define meaningful KPIs, success metrics, and measurement frameworks and establish reliable mechanisms for tracking them.</b></p><p><b>•SQL & Data Exploration: Write advanced SQL queries to extract, join, transform, and analyze large-scale datasets, including complex joins, CTEs, window functions, aggregations, and query optimization.</b></p><p><b>•Data Pipeline & Strategy: Collaborate with Data Engineers to define data requirements, improve analytical datasets and pipelines, and maintain high standards of data quality, consistency, and integrity.</b></p><p><b>•Experimentation & Advanced Analytics: Support the design and analysis of A/B tests, experiments, cohort analyses, and other statistical evaluations to guide product and business decisions.</b></p><p><b>•Root-Cause Analysis: Investigate unexpected changes in business or product metrics, identify underlying drivers, and recommend corrective actions or opportunities for improvement.</b></p><p><b>•Stakeholder Communication: Translate complex analytical findings into clear business insights and present recommendations to both technical and non-technical stakeholders, including senior leadership.</b></p><p><b>•Cross-Functional Collaboration: Work closely with Product, Engineering, Data Engineering, BI, Operations, and Business teams to solve ambiguous analytical problems.</b></p><p><b>•Google Collaboration: Work effectively with Google teams and stakeholders, understanding their analytical processes, data environment, engineering practices, and expectations.</b></p><p><b>•3+ years of experience in Data Analytics, Business Intelligence, Product Analytics, or a related analytical role.</b></p><p><b>•Bachelor's degree in a quantitative discipline such as Statistics, Mathematics, Computer Science, Economics, Operations Research, Engineering, or equivalent practical experience.</b></p><p><b>•Advanced proficiency in SQL, including:</b></p><p><b>oComplex joins and aggregations</b></p><p><b>oCTEs and subqueries</b></p><p><b>oWindow functions</b></p><p><b>oData transformations</b></p><p><b>oQuery optimization</b></p><p><b>•Hands-on experience with Python or R for data analysis, statistical analysis, automation, or advanced analytics.</b></p><p><b>•Strong experience developing dashboards, reports, KPIs, and analytical solutions for business and product stakeholders.</b></p><p><b>•Ability to analyze large and complex datasets and translate findings into actionable business recommendations.</b></p><p><b>•Strong understanding of data quality, validation, metric definitions, and analytical best practices.</b></p><p><b>•Excellent communication skills with the ability to explain complex analytical concepts to technical and non-technical audiences.</b></p><p><b>•Strong problem-solving skills and ability to independently investigate ambiguous business questions.</b></p><p><b>Google Experience</b></p><p><b>Candidates with prior experience working directly with Google teams or supporting Google projects/programs are strongly preferred.</b></p><p><b>Experience with Google's internal data and analytics ecosystem—particularly tools such as F1 Query and Plx/PLX dashboards—will be a significant advantage.</b></p><p><b>Experience working within Google's engineering, product, or data-driven decision-making environment is also highly desirable.</b></p><p><b>Preferred / Nice-to-Have Skills</b></p><p><b>Experience in one or more of the following areas is a plus:</b></p><p><b>•Google Cloud Platform (GCP), particularly BigQuery</b></p><p><b>•F1 Query</b></p><p><b>•Plx / PLX dashboard ecosystem</b></p><p><b>•Looker / Looker Studio</b></p><p><b>•Google Sheets and other Google analytical tools</b></p><p><b>•Data visualization and dashboard design best practices</b></p><p><b>•Statistical experimentation and A/B testing</b></p><p><b>•Cohort, funnel, segmentation, and behavioral analysis</b></p><p><b>•Predictive analytics and statistical modeling</b></p><p><b>•Machine learning concepts and applications</b></p><p><b>•Data pipeline and data-engineering fundamentals</b></p><p><b>•Experience analyzing high-volume datasets at enterprise scale</b></p><p></p><p><b>•Automation of recurring analytical and reporting workflows</b></p><div><br /></div><div><br /></div>