We are looking for a Data Analytics Engineer to join our Data team and help design, build, and maintain high-throughput, scalable data pipelines and streaming infrastructure. The ideal candidate brings deep hands-on experience with modern data stack technologies and can operate independently in a fast-paced environment.

As an Analytics Engineer you will architect our analytical data foundation, acting as the bridge between raw infrastructure and data analytics. You will transform complex, high-volume banking data into reliable, governed, and high-performance models. Your focus will be on designing scalable data transformations and structuring business-ready models that turn fragmented datasets into trusted, reusable assets. Your ultimate mission is to empower every data analyst and decision-maker with a consistent single source of truth—driven by standardized definitions and robust logic—across both traditional relational systems and modern large-scale storage environments.

Responsibilities:

• Analytical Data Architecture: Design and implement robust data models (facts, dimensions, materialized views) that serve as building blocks for all downstream reporting and analysis.
• SQL-Driven Transformations: Develop and maintain performant transformation logic that converts raw operational data into business-ready assets, emphasizing code reusability, modularity, maintainability, and efficient execution at scale.
• Workflow Orchestration & Automation: Build and manage automated data pipelines ensuring timely data delivery, with robust error handling, dependency management, and recovery patterns.
• Data Quality & Observability: Implement automated validation frameworks to monitor data integrity, freshness, and accuracy, proactively identifying and resolving issues before they impact stakeholders.
• Semantic Layer Management: Define and govern centralized business logic and metrics within a unified semantic layer. Ensure KPIs mean the same thing across every dashboard and department.
• Dashboarding & BI Delivery: Design and maintain intuitive, performant dashboards and reports that translate curated data into actionable insights for business stakeholders.
• Governance & Documentation: Maintain comprehensive data catalogs and lineage documentation, providing transparency into how data is transformed and consumed.

 Qualification:

• Experience: 4+ years of experience in one or more of the following: analytics engineering, data engineering, BI development, or highly technical analytics roles.
• Advanced SQL: Expert-level proficiency including advanced analytical functions, query tuning, and managing complex transformation logic.
• Python: Ability to write scripts for data transformation, custom automation, data validation, or extending orchestration capabilities.
• Databases: Hands-on experience with:

1. Row-based databases such as PostgreSQL, including schema modeling and performance tuning via indexing, partitioning, and query optimization
2. Columnar databases such as ClickHouse, including MergeTree family table engines and related performance-oriented features. 

• Workflow Orchestration: Production experience with Apache Airflow (authoring DAGs, managing schedules, troubleshooting complex workflows).
• BI Tooling: Experience designing and optimizing analytics-ready data for BI platforms such as Metabase, including semantic modeling and materialized views for end-user self-service.
• Engineering Best Practices: Proficiency with Git for version control and commitment to code reviews and technical documentation.

 NICE TO HAVE

• NoSQL & Search: Experience with Elasticsearch and NoSQL databases (MongoDB), including transforming data from these sources. 
• Data Lake: Practical experience with Data Lake architecture, including partitioning strategies, file formats (Parquet), and data layering (Raw, Silver, Gold).
• FinTech Experience: Background in banking or financial services data models.

Benefits:

• Work from home option
• Flexible working hours
• Training courses and professional development opportunities
• Military service project (Limited)
• Supplemental health insurance
• Team-building budget
• Performance-based bonuses
• Loans
• Lunch subsidies