Job VC
Analytics Engineer
Technologies
Description
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)
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)