Job Description The Role The Senior Data Analyst turns complex data into clear insights that improve business decisions. This role partners with stakeholders to clarify business questions, identify the right data sources, analyze performance drivers, and translate findings into practical recommendations. Using SQL, Databricks, Python, visualization tools, and strong business judgment, the Senior Data Analyst builds trusted reporting, monitors KPIs, investigates trends and root causes, and helps business teams act with greater speed and confidence. What You’ll Do - Analyze large, complex datasets to identify trends, anomalies, risks, opportunities, and performance drivers that inform business decisions. - Translate ambiguous business questions into structured analytical plans, including hypotheses, KPI definitions, data requirements, and success measures. - Use advanced SQL and Data Bricks to query, join, transform, validate, and reconcile data across multiple sources and business systems. - Leverage Databricks, Python, and similar analytics platforms to perform data wrangling, automate repeatable analyses, and support scalable performance measurement. - Investigate unexpected metric movements, data quality issues, and reporting discrepancies by validating source logic, transformations, and assumptions. - Build dashboards, reports, scorecards, and self-service analytics tools that make KPIs, trends, and business performance easy to understand and act on. - Apply data visualization and storytelling best practices to create clear executive summaries, intuitive dashboards, and actionable insight narratives. - Partner with business stakeholders to clarify goals, decision needs, pain points, and priorities, then translate them into practical analytics solutions. - Collaborate with data engineering and platform teams to define source data requirements, transformation logic, semantic model needs, and data quality expectations. - Communicate findings to technical and non-technical audiences with clear recommendations, business implications, and next steps. Your Skills & Abilities (Required Qualifications) - Bachelor’s degree in Business Analytics, Data Science, Statistics, Information Systems, Computer Science, Engineering, or a related field. - 6+ years of experience in data analytics, business intelligence, product analytics, or a related analytical role. - Experience leveraging AI-enabled tools to accelerate data analysis, improve analytical productivity, generate hypotheses, automate repetitive tasks, and enhance insight generation while applying sound judgment to validate outputs. - Advanced SQL experience, including querying, joining, transforming, and validating large, complex datasets across multiple sources. - Hands-on experience using analytics platforms such as Databricks, Python, or similar tools to perform data analysis, automation, and recurring performance measurement. - Experience building dashboards, reports, scorecards, or semantic models using tools such as Power BI, Tableau, Looker, or similar visualization platforms. - Strong analytical problem-solving skills, including the ability to identify trends, anomalies, root causes, risks, opportunities, and performance drivers. - Experience translating ambiguous business questions into structured analyses, KPI definitions, data requirements, and actionable recommendations. - Demonstrated ability to partner with business stakeholders, understand decision needs, and communicate insights clearly to technical and non-technical audiences. - Experience validating data quality, reconciling discrepancies, documenting assumptions, and ensuring reporting outputs are trusted and repeatable. - Strong written and verbal communication skills, with the ability to simplify complex findings into executive-ready summaries and practical next steps. What Can Give You a Competitive Advantage (Preferred Qualifications) - Operates as a strategic thought partner, not just a report builder, by understanding the business context behind every request and proactively identifying better questions, sharper metrics, and more meaningful ways to measure success. - Connect data to decisions by clearly explaining what the numbers mean, why they matter, what trade-offs exist, and which actions stakeholders should consider next. - Anticipates stakeholder needs before they are explicitly requested by spotting recurring questions, automating repeatable insights, and creating dashboards that reduce manual follow-up and improve decision speed. - Builds trust in the data by challenging assumptions, validating definitions, documenting logic, and ensuring stakeholders understand the source, limitations, and appropriate use of each metric. - Simplifies complexity by turning messy data, unclear requirements, and competing priorities into clean analysis, focused storytelling, and practical recommendations. - Raises the quality of analytics across the team by sharing best practices, mentoring others, improving dashboard standards, and helping create consistent ways of defining, measuring, and communicating performance. - Demonstrates strong ownership by following problems through resolution, ensuring insights are acted on, and continuously improving reports and dashboards based on stakeholder feedback and business impact. 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