Data Analyst

About the Role 

• Build and validate the data logic behind core business KPIs — translating business definitions into certified analytical models across multiple dimensions. 

• Perform end-to-end data reconciliation — validating analytical datasets against finance and operational source-of-truth systems, investigating discrepancies, and ensuring accuracy of reported metrics. 

• Conduct customer lifecycle analysis — defining and measuring customer populations, segmenting behavior across acquisition, engagement, and renewal stages, and identifying drivers of key business outcomes. 

• Build waterfall and funnel analyses — decomposing aggregate metric movements into contributing factors, quantifying the impact of each, and presenting findings to leadership in a clear and actionable format. 

• Build and maintain data models and transformation logic — working in Databricks to create, migrate, and backfill analytical tables, validate data transformations, and ensure consistency between raw and curated data layers. 

• Produce and maintain automated dashboards and reports — building executive-facing visualizations that track business performance across multiple data sources with automated refresh pipelines. 

• Conduct root cause analysis on metric anomalies — investigating unexpected movements in KPIs, tracing issues through multiple data layers, and producing actionable findings for business stakeholders. 

• Deliver ad-hoc analysis for senior leadership — responding to executive requests with data-driven insights on topics such as customer sizing, audience segmentation, and business performance deep dives. 

• Document data lineage and business logic — mapping how metrics flow from source systems through transformation layers to final reporting, ensuring traceability and auditability of all certified KPIs. 

• Collaborate cross-functionally with Data Engineering, Finance, and Data Stewards to certify data logic before it enters production reporting, and flag data quality issues proactively. 

About You 

• You have 5+ years of experience in data analytics, business intelligence, or a related quantitative field. 

• You have deep expertise in subscription or SaaS business metrics — including retention rate, churn, auto-renewal, trial conversion, LTV, and bookings — ideally in a consumer technology or eCommerce context. 

• Data and coding fluency — you are a self-starter comfortable using SQL, Python, and generative AI tools to access data and deliver analysis independently. 

• You have hands-on experience with Databricks (SQL,Python, PySpark, Delta Lake) and can write, review, and troubleshoot complex analytical queries and data pipelines. 

• You have experience with BI and reporting platforms — particularly Power BI. 

• You have experience with data validation and reconciliation — comparing data across source systems, identifying discrepancies, and documenting business rules that govern metric calculations. 

• You are comfortable performing root cause analysis — diagnosing why a metric moved, tracing issues through multiple data layers, and presenting findings clearly to both technical and non-technical stakeholders. 

• You can translate ambiguous business questions into structured analytical approaches — scoping the data needed, defining assumptions, and delivering concise answers under time pressure. 

• You have experience working in agile environments — managing your own backlog across 2-week sprints, balancing recurring reporting with project work, and communicating progress and blockers proactively. 

#LI-Hybrid