Overview

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.

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
  • Knowledge of BI tools such as Tableau, Power BI, Looker, or similar
  • Knowledge of data quality testing, validation, documentation, and lineage practices
  • Good understanding of analytical database design
  • 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
Nice To Have:
  • 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
  • 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
  • Familiarity with cloud platforms or Big Data technologies would be a plus
Skills:
  • Problem-solving
  • SQL development
  • Data modeling
  • Dimensional modeling
  • Semantic modeling
  • Data quality testing
  • Communication
Technologies:
  • SQL
  • MS SQL Server
  • PostgreSQL
  • dbt
  • Tableau
  • Power BI
  • Looker
  • Git
  • Python
  • Airflow
  • Dagster
  • Prefect
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