Work directly with business stakeholders to understand their domain and translate questions into data solutions — delivering iteratively from quick explorations to production-ready data products
Design and build data models (dimensional models, semantic layers) and implement them as Snowflake views, DBT marts, and Power BI datasets
Build and optimize Power BI semantic models (DAX, Power Query) and design reports that surface your models effectively
Write consumption-layer SQL (views, transforms, analytical queries) across Snowflake and SQL Server to serve downstream analytics
Consolidate data solutions into modern analytics platforms, improving consistency and reducing technical debt
Define and document metric definitions, business logic, and data product specifications
Design and implement data quality tests (dbt tests, SQL validations) and contribute to the team's testing standards and governance framework
Collaborate with Data Engineers through code reviews, shared planning, and PR-based workflows

Bachelor's degree in Computer Science, Data Analytics, Statistics, Mathematics, Economics, or related quantitative field
3–5 years of experience in analytics engineering, data analysis, BI engineering, or related roles
Curiosity and drive to deeply understand the business domains you support
Strong SQL skills: complex queries, window functions, CTEs, performance tuning across cloud data warehouses
Experience with data modeling — dimensional models, semantic layers (Power BI datasets, SSAS tabular), or structured consumption models
Proficiency in Power BI: report design, DAX, Power Query, and data model optimization
Understanding of data visualization best practices — you care about how information is presented, not just that it renders
Experience with Snowflake or a similar cloud data platform
Experience working across multiple data sources and environments (data warehouses, operational databases, APIs)
Familiarity with Git-based development workflows — branching, pull requests, code review, and working across dev/staging/prod environments
Comfortable presenting to business stakeholders and explaining data concepts in plain language
Comfortable working in agile sprints with iterative delivery

Experience using AI coding agents (GitHub Copilot, Codex, Claude Code, or similar) to accelerate development, analysis, or documentation
Experience with DBT for data transformations
Python for data analysis, scripting, or automation
Experience with MicroStrategy reporting or administration
Familiarity with data catalog or data governance tools
Background in telecommunications, SaaS, or B2B industries

Analytics Engineer

Key skills