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)
