About Us

We build AML (Anti-Money Laundering) solutions that help financial institutions stay compliant with local and global regulatory rules and meet expectations of audit and regulatory inspections. Our product combines industry best practices, regulator-accepted methodology, and a practical, user-centric design.

As we grow, we are hiring a Senior Data Analyst to support and share responsibilities across data analysis, data pipelines, and analytical methodology development, working closely with product, engineering, and client-facing teams.

Role Overview

This position is designed for someone who is curious, analytical, and willing to work across different parts of the data lifecycle. You will be exposed to transaction data, data pipelines, analytics, and AML methodologies. You will work closely with team members and will be responsible to help shape how data is used in the product and how analytical ideas are translated into practical solutions.

Key Responsibilities

• Lead analysis of source and product data to evaluate data quality, structure, lineage, and fitness for regulatory and analytical use

• Design, oversee, and validate ETL architecture and data transformation logic, ensuring scalability, accuracy, and auditability

• Develop, optimize, and review complex SQL queries to support ETL processes, data reconciliation, advanced analytics, and ad-hoc investigations

• Own end-to-end data pipeline reliability, including data flow governance, testing strategies, root-cause analysis, and production issue resolution

• Drive the design and refinement of AML transaction monitoring methodologies, including rule logic, case-scoring models, threshold calibration, and anomaly-detection frameworks

• Define and review analytical outputs and visualizations used in product features, internal decision-making, regulatory reporting, and client communications

• Act as a key liaison between data, product, and engineering teams, translating regulatory and analytical requirements into scalable, implementable technical solutions

• Requirements

• Strong expertise in data analysis and data engineering principles, including the design and optimization of ETL processes

• Advanced working knowledge of SQL and relational data models, with the ability to analyze, optimize, and validate complex datasets

• Proven understanding of data pipeline architectures and data quality management, including validation, reconciliation, and control frameworks

• Demonstrated experience applying analytics to real-world problem domains, such as AML transaction monitoring, anomaly detection, and risk analytics

• Ability to evaluate, adopt, and guide the use of new tools, technologies, and data methodologies in evolving business and regulatory environments

• Comfortable operating across both data and system-level considerations, bridging analytical requirements with production-grade system design

• Professional working proficiency in English (written and spoken)

Tools That We Use

• Data Querying & Analysis: SQL, Excel, Python

• Databases: PostgreSQL, ClickHouse, Oracle

• Programming & Framework: Java, Python, Angular, typescript

• Visualization & Analysis Tools: Python, Jupyter Notebook, Streamlit

• Development & Infrastructure Tools: Git, GitLab, Docker, ELK

• Team Collaboration Tools: Slack, Notion, Jira