Overview

Work Schedule Standard (Mon-Fri)

Responsibilities

• Partner with business stakeholders and senior analytics team members to understand business questions, reporting requirements, KPIs, and analytical needs.
• Analyze large and complex datasets to identify trends, patterns, opportunities, anomalies, and drivers of business performance.
• Develop recurring and ad hoc analyses supporting commercial, customer, digital, operational, and strategic initiatives.
• Build and maintain dashboards, reports, scorecards, and visualizations that provide clear and actionable insights to business stakeholders.
• Write and maintain SQL queries and data transformations used to create analytical datasets and support reporting and analysis.
• Assist in developing reusable analytical datasets and data models that improve consistency and reduce duplicated reporting logic.
• Support the development and maintenance of a trusted semantic analytics layer, including business definitions, KPIs, dimensions, measures, hierarchies, and relationships.
• Work with senior analysts and business stakeholders to document and validate metric definitions and ensure consistent interpretation across analytical products.
• Perform data profiling, validation, reconciliation, and quality checks to identify inconsistencies, missing data, or unexpected results.
• Investigate data-quality and reporting issues and partner with analytics engineering and data engineering teams to identify root causes.
• Support the creation and maintenance of documentation covering data sources, transformations, metric calculations, business rules, assumptions, and analytical logic.
• Assist with ETL/ELT and analytics engineering activities, including data transformations, testing, validation, and maintenance of curated analytical datasets.
• Use Python or similar analytical technologies for data preparation, automation, exploratory analysis, validation, and analytical workflows as appropriate.
• Support customer, product, digital, marketing, commercial, and operational analytics initiatives through data exploration and performance measurement.
• Assist with experimentation, segmentation, funnel analysis, customer journey analysis, KPI tracking, forecasting, and other analytical methodologies as needed.
• Work with senior team members to identify opportunities to automate manual reports, repetitive analysis, and data-preparation processes.
• Support the transition from traditional reporting toward more self-service, proactive, and AI-enabled analytical experiences.
• Participate in the development and testing of conversational analytics, natural-language querying, AI-generated insights, semantic search, and other emerging analytical capabilities.
• Validate AI-generated analytical outputs by comparing responses against trusted datasets, established metrics, source systems, and documented business rules.
• Help organize metadata, business definitions, and semantic context so AI-enabled analytics solutions can more accurately interpret enterprise data.
• Collaborate with data engineering, architecture, security, product, and governance teams to ensure analytical solutions follow organizational standards.
• Participate in peer reviews, testing, documentation, and continuous improvement of analytics products and processes.
• Communicate analytical findings clearly to both technical and non-technical stakeholders using effective visualizations, summaries, and recommendations.
• Continuously develop knowledge of modern analytics, data engineering, semantic technologies, cloud data platforms, generative AI, and emerging analytical practices.
• Supporting the definition and maintenance of reusable business metrics and KPIs.
• Helping document business terminology and map business concepts to enterprise data.
• Assisting in defining relationships between customers, products, channels, transactions, campaigns, digital interactions, and other business entities.
• Building analytical datasets that can be reused across dashboards, reports, analyses, and AI-enabled applications.
• Helping identify differences between business terminology and source-system definitions.
• Contributing metadata, definitions, descriptions, and contextual information that improve self-service and AI-enabled analytics.

Qualifications

• Bachelor's degree in Computer Science, Data Science, Engineering, Information Systems, Business Analytics, Statistics, Mathematics, Economics, or a related quantitative discipline.
• Equivalent combination of education and relevant professional experience may be considered.
• Advanced degree or relevant professional certifications are beneficial but not required.
• 2–5 years of experience in data analytics, business intelligence, analytics engineering, data engineering, data science, or a related discipline.
• Experience working with structured datasets and translating business questions into analytical outputs.
• Experience developing dashboards, reports, analyses, or analytical datasets in a business environment.
• Experience working with relational databases, cloud data warehouses, or enterprise analytical platforms.
• Exposure to data modeling, dimensional modeling, semantic models, metrics layers, or curated analytical datasets is preferred.
• Experience with cloud-based data platforms such as Snowflake, Teradata, Hadoop, AWS, Azure, Google Cloud Platform, or similar technologies is beneficial.
• Experience with digital analytics technologies such as Google Analytics or Adobe Analytics is beneficial.
• Exposure to enterprise systems such as SAP, ERP, CRM, digital commerce, marketing, customer, or operational platforms is preferred.
• Exposure to generative AI, conversational analytics, natural-language querying, semantic search, machine learning, or AI-enabled analytics is beneficial
• Strong SQL skills, including joins, aggregations, subqueries, common table expressions, window functions, and analytical querying.
• Working knowledge of Python or another analytical programming language for data manipulation, automation, analysis, or validation.
• Experience with visualization and business intelligence technologies such as Tableau, Power BI, or similar tools.
• Understanding of data structures, relational databases, analytical datasets, and basic data modeling concepts.
• Familiarity with ETL/ELT concepts and modern data-transformation workflows.
• Understanding of data-quality principles, including validation, reconciliation, completeness, consistency, and accuracy.
• Familiarity with version control, documentation, testing, or collaborative software/data development practices is beneficial.
• Basic understanding of cloud data architectures and modern data warehouse technologies.
• Interest in or exposure to generative AI, large language models, semantic search, embeddings, knowledge models, or AI agents is preferred.
• Ability to understand how business definitions, metadata, data relationships, and metric calculations affect analytics and AI-generated insights.