Analytics Engineer

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

1. Interview with a Recruiter (1 hour)
2. Technical Interview with a Hiring Manager (1.5 hours)
3. Final Interview with CTO (optional)
4. Refference Check

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