Analytics Engineer

JOB DESCRIPTION An Analytics Engineer will support the Marketing Department by building trusted, reusable, and well-documented analytical data models for business users. The role will focus on semantic modeling, modeled data marts, SQL-based transformations, data quality, testing, documentation, and reliable data foundations for reporting, analytics, and data science use cases. JOB RESPONSIBILITIES • Study the best foreign experience and modern approaches in analytics engineering, data modeling, and data reliability; • Design and maintain clean, reusable, and business-ready analytical data marts; • Develop and maintain metric logic, and semantic models; • Build SQL-based transformation workflows using modular SQL, staging layers, views, stored procedures, reusable transformations, and dbt-style practices where applicable; • Create source-of-truth datasets for dashboards, reports, analytics, and data science use cases; • Implement data quality checks, tests, validation rules, and reconciliation processes; • Document data models, business definitions, data lineage, and usage rules; • Support data reliability, consistency, and transparency across reporting and analytical outputs; • Collaborate with analysts, data scientists, and business users to improve self-service analytics; • Support integration of analytical data models with BI tools such as Tableau, Power BI, Looker, or similar; • Support scheduling, orchestration, and monitoring of analytical data workflows when needed; • Contribute to better data governance, version control, and maintainability of analytical assets. REQUIRED QUALIFICATIONS • Bachelor’s Degree in a technical related field; data analytics, computer science, mathematics, economics, or engineering background is a plus; • Strong analytical and problem-solving skills; • Excellent knowledge of SQL; • Strong experience with SQL development, preferably with MS SQL Server and/or PostgreSQL; • Experience with advanced SQL development, including CTEs, views, stored procedures, reusable transformations, and query optimization; • Good understanding of data modeling, dimensional modeling, data marts, and analytical warehouse concepts; • Experience with metric logic, semantic modeling, or business data definitions; • Understanding of dbt-style analytics engineering practices, including modular SQL, staging layers, intermediate layers, modeled marts, testing, documentation, and lineage would be a strong plus; • Knowledge of BI tools such as Tableau, Power BI, Looker, or similar; • Knowledge of data quality testing, validation, documentation, and lineage practices; • Understanding of Git, version control, and code review practices would be a plus; • Knowledge of Python for data manipulation or automation would be a plus; • Familiarity with Airflow, Dagster, Prefect, or other orchestration tools would be a plus; • Good understanding of analytical database design; • Familiarity with cloud platforms or Big Data technologies would be a plus; • Ability to explain data definitions, and analytical findings to business users; • Ability to work independently and collaboratively; • Excellent knowledge of Armenian, Russian, and English languages.