Senior Data Analyst  

The Senior Data Analyst is a strategic contributor responsible for leading the delivery of insight-driven analytics solutions. This role translates business questions into structured data inquiries, applies rigorous analytical techniques, and delivers clear, actionable recommendations that inform decisions across business units and client engagements.

As a candidate, you combine analytical depth, business acumen, and stakeholder fluency to maximize the value of data in dynamic, collaborative environments.

Key Responsibilities:

Analytical Execution and Solutioning

• Lead the execution of advanced analytical workflows including univariate, bivariate, and multivariate statistical analysis.
• Perform comprehensive exploratory data analysis (EDA) to identify trends, anomalies, and key value drivers.
• Design and implement statistical models such as regression, clustering, and time-series forecasting to uncover insights and predict behavior.
• Deliver compelling data visualizations and narratives that enable data storytelling across technical and non-technical audiences.
• Apply domain-specific techniques such as geospatial analysis, natural language processing, market basket analysis, and time-based cohort analysis when relevant.

Strategic Collaboration and Design Facilitation

• Translate high-level business objectives into technical requirements and data-driven approaches.
• Lead collaborations with data engineers, scientists, and BI developers to provide scope, share context, and ensure alignment across functions.
• Validate analytical outputs and co-define data product features or project deliverables with stakeholders.
• Draft and design high-level data architectures that ensure guidance for technical developers and interpretability for business owners.
• Manage internal and external stakeholder expectations around features, iterations, and success metrics.
• Ensure adherence to identified practices in documentation, versioning, code standards, and reproducibility. Be able to propose and own these practices when needed.
• Mentor and coach junior analysts and cross-functional peers in analytical thinking, tooling, and structured problem-solving.
• Exhibit strong interpersonal skills, empathy, and responsiveness in high-collaboration and client-facing environments.

Impact Ownership and Value Realization

• Own the definition, monitoring, and reporting of key performance indicators (KPIs) tied to data project success.
• Support business units and clients in interpreting results, enabling strategic choices and operational improvements based on analytics.
• Proactively identify data-driven opportunities and support the development of business cases or pilot experiments.

#li-hybrid

• Minimum of 5 years of progressive experience in data analytics, business intelligence, or applied quantitative roles, with proven ownership of end-to-end analytical solutions and stakeholder-facing deliverables.
• Advanced skills in SQL for data extraction, transformation, and analysis across relational databases.
• Strong programming capability in Python, particularly with libraries for statistical modeling, data manipulation, and visualization (e.g., numpy, scipy, pandas, matplotlib, seaborn).
• Demonstrated experience designing or building dashboards or visual reports using BI tools such as Power BI, Tableau, or Looker.
• Hands-on experience with at least one cloud data platform (e.g., Snowflake, Databricks, BigQuery, Redshift, or Azure Synapse).
• Applied understanding of core statistical techniques (e.g., regression, clustering, time-series analysis, hypothesis testing).
• Experience in conducting domain-specific analyses such as geospatial, text, market basket, or time-based cohort analysis.
• Ability to structure ambiguous problems, validate assumptions, and derive insights aligned with business value.
• Experience working in cross-functional analytics teams, collaborating with data engineers, scientists, and business stakeholders.
• Familiarity with structured development workflows, including version control (e.g., Git) and agile or iterative project delivery.
• Strong ability to communicate analytical outputs clearly, facilitate alignment across technical and non-technical teams, and mentor peers in best practices.