Senior Data Quality Engineer in financial markets

TradingView is a global financial analysis and charting platform used by more than 100 million users across over 180 countries. It provides tools for charting, market data, collaboration, publishing, and analyzing financial markets.

• Define and maintain data quality and observability requirements, metrics, and SLAs;
• Establish monitoring and controls for data accuracy, completeness, consistency, timeliness, and reliability;
• Build and maintain monitoring, dashboards, and alerts for anomalies, data drift, duplicates, missing data, delayed data, and other quality issues;
• Own the data incident lifecycle from detection and initial assessment through investigation, root cause analysis, remediation, and preventive actions;
• Understand data ingestion, transfer, processing, transformation, storage, and consumption stages and their impact on data quality;
• Expand automated data quality checks and operational controls to detect issues early, reduce manual work, and improve validation coverage;
• Collaborate with developers, product managers, analysts, and other teams to investigate data issues, clarify requirements, improve operational processes, and drive long-term quality improvements;
• Maintain and evolve documentation for data quality and monitoring requirements, incident management processes, investigation findings, and recurring-issue prevention.

• 5+ Years of experience in DataOps, Data Quality, Data Engineering, Data Analytics, or a related field;
• Strong practical experience in data operations and troubleshooting;
• Strong understanding of data quality dimensions, including completeness, accuracy, consistency, timeliness, and validity;
• Strong understanding of data observability principles and monitoring approaches;
• Strong understanding of quality assurance principles and hands-on experience with test design, data validation, edge-case identification, and systematic issue investigation;
• Strong understanding of the data incident lifecycle, including detection, triage, investigation, root cause analysis, remediation, and prevention of recurring issues;
• Good understanding of data lifecycles and processing pipelines, including ETL, batch, and streaming approaches;
• High comfort level working in the terminal and Linux environment;
• Experience using command-line utilities to process and analyze text and tabular data and analyze application and system logs;
• Solid knowledge of JSON/YAML, Git/GitHub, CI/CD practices such as GitLab and Jenkins, and general SDLC concepts;
• Confident use of Python, Bash, and regular expressions for data processing, incident investigation, and quality control automation;
• Strong understanding of financial data and financial markets;
• B2+ English proficiency with strong reading and written technical communication skills;
• Nice to have: Experience with Kafka, data observability tools, data catalogs, metadata management including OpenMetadata, dbt, data quality frameworks, automated validation systems, AI tools, AI-assisted engineering workflows, TradingView or similar financial-market analysis products.

• Flexible working hours;
• Well-equipped offices for focused and collaborative work;
• A global, distributed team of 500+ professionals;
• Learning, mentorship, and long-term career growth;
• Relocation support;
• Private health insurance;
• Performance-based bonuses;
• TradingView Premium access;
• Regular team events and company-wide meetups.