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Описание: |
We are looking for a skilled Analytics Engineer to join our team and help us build a worldclass data foundation. You will sit at the intersection of data engineering and business analysis, transforming raw data into high-quality, actionable datasets within our warehouse. Your goal is to establish a “gold standard” for our data assets, ensuring they are reliable, welldocumented, and ready for strategic decision-making by Departments and Senior Management.
Requirements
* Expert SQL: Advanced proficiency in SQL, including window functions, complex joins, indexing, and OLAP query optimization; 4+ years
* Data Stack: Mandatory hands-on experience with dbt (models, tests, macros)
* Data Ingestion & Extraction: Practical experience with the dlt (data load tool) library or similar Python-based ingestion frameworks
* Orchestration: Experience managing data workflows with Dagster (preferred) or similar orchestrators such as Airflow or Prefect
* Data Warehousing: Solid understanding of OLTP vs. OLAP and data modeling techniques
* BI Development: Experience designing data sources and interactive dashboards in Metabase, Tableau, or similar tools
* Python: Proficiency in writing clean Python code for data manipulation and pipeline automation
* Engineering Best Practices: Proficiency with Git (Pull Requests, Code Review), CI/CD, and Docker
* Systematic Thinking: Strong attention to detail and the ability to build scalable, logical systems
* Requirement Formalization: Ability to gather and formalize requirements from stakeholders, even when they are not yet fully defined
* Business Acumen: Focus on identifying business growth or risk drivers and preparing reports for senior management
Responsibilities
* Design and implement analytics-ready data models using Fact/Dimension tables and semantic layers
* Transform raw datasets into clean, structured marts using dbt as the primary transformation tool
* Ensure absolute consistency and logic alignment between the Data Warehouse (BigQuery) and the BI layer (Metabase)
* Write, test, and optimize complex SQL queries for advanced analytical use cases and reporting
* Leverage Views and Materialized Views to improve performance and optimize BigQuery resource consumption
* Partner with stakeholders, especially the Risk Department, to translate business requirements into robust technical data models
* Support and extend automated data ingestion flows from various sources using dlt
* Manage and monitor the lifecycle of data assets and pipeline dependencies within Dagster
* Define and standardize core business metrics and KPIs at the code level to ensure a “Single Source of Truth”
* Implement automated data quality checks, validation rules, and proactive monitoring at the analytics layer
* Document business logic, data definitions, and KPI catalogs for company-wide data discovery
Nice to have
* FinTech Domain Experience: Previous experience working with financial transactions, digital wallets, or fraud detection systems.
* Kubernetes Awareness: Basic understanding of how containers are deployed and managed in a K8s environment.
* Regulatory Awareness: Understanding of data privacy and security standards in financial services.
Benefits
* Competitive and attractive compensation
* Remote work schedule
* Proper rest time with 24 annual leave days
* Challenging and unique tasks in the FinTech field
* Funding for gym memberships to support a healthy work-life balance
Interview Stages
* Interview with a Recruiter (1 hour)
* Interview with a Hiring Manager (1.5 hours)
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