Senior Data Analyst – Analytics Engineering & AI

Role Overview

The Senior Data Analyst will partner closely with business stakeholders, data engineers, architects, product teams, and technology partners to transform complex business questions into trusted data products, scalable analytical solutions, reusable semantic models, and actionable insights.

What You Will Do

Translate complex business questions into analytical requirements, data models, metrics, dashboards, data products, and actionable recommendations. Develop advanced analyses that identify trends, opportunities, root causes, customer behaviors, operational drivers, and areas for performance improvement.

Why It Might Be a Fit

The ideal candidate combines strong analytical thinking and business acumen with hands-on SQL, data modeling, analytics engineering, visualization, and modern AI/data capabilities.

Requirements

• Bachelor’s degree in Computer Science, Data Science, Engineering, Information Systems, Business Analytics, Statistics, Mathematics, Economics, or a related quantitative discipline.
• Master’s degree in a quantitative, technical, or business discipline preferred.
• 6+ years of experience in data analytics, business intelligence, analytics engineering, data engineering, data science, or a related discipline.
• Demonstrated experience translating ambiguous or complex business problems into structured analytical solutions.
• Strong experience working with enterprise-scale data environments and large, complex datasets.
• Experience developing analytical data models and curated datasets for reporting, analytics, and downstream consumption.
• Experience working with cloud-based data warehouses or analytical platforms such as Snowflake, Teradata, Hadoop, AWS, Azure, Google Cloud Platform, or similar technologies.
• Experience designing or working with semantic models, metrics layers, dimensional models, business metadata, or governed analytical datasets.
• Experience supporting AI-enabled analytics, conversational analytics, natural-language-to-data experiences, generative AI applications, or semantic search is preferred.
• Experience integrating analytical data with enterprise systems such as SAP, ERP, CRM, digital commerce, marketing, customer, or operational systems is preferred.
• Advanced SQL skills, including complex transformations, joins, window functions, optimization, reconciliation, and analytical querying.
• Proficiency with Python for analytics, data manipulation, automation, validation, or analytical application development.
• Strong understanding of data modeling, including dimensional modeling, fact/dimension structures, analytical datasets, and reusable business entities.
• Understanding of modern ETL/ELT and analytics engineering practices, including transformation pipelines, testing, documentation, version control, and deployment.
• Experience with visualization and business intelligence technologies such as Tableau, Power BI, or similar platforms.
• Understanding of data quality, data lineage, metadata management, governance, and master/reference data concepts.
• Knowledge of APIs, structured and semi-structured data, cloud data architectures, and modern data integration patterns.
• Familiarity with generative AI, large language models, retrieval-based architectures, semantic search, embeddings, knowledge models, or AI agents is preferred.
• Understanding of how metadata, business terminology, semantic relationships, metric definitions, and governed data influence the accuracy and reliability of AI-generated analytical responses.

Benefits

• Dental insurance
• Vision insurance
• Health insurance
• Retirement plan
• Paid time off
• Vacation days
• Holidays
• Life insurance
• Disability insurance
• Stock options
• Equity participation

Tagged as: Data Analysis