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Senior Data Quality Engineer
senior · Technology / Software Development
TradingView is the world’s largest financial analysis platform with more than 100M users across 180+ countries.
We build tools that help traders and investors make informed decisions — from advanced charting and market data to collaboration and publishing features. Our products are used daily by millions of individuals and trusted by companies like Revolut, Binance, and CME Group.
We’re continuing to grow and scale our platform, and we’re looking for people who care about product quality, take ownership of their work, and want to build systems used by a global audience.
About the teamWe are looking for a Senior Data Quality Engineer to take ownership of data quality, observability, and operational reliability across the data lifecycle.
In this role, your primary focus is the data itself: monitoring its quality and health, using structured approaches to data validation and applying QA practices, identifying and investigating anomalies, resolving data-related incidents, and improving processes to prevent issues from recurring. You will work across the data lifecycle and collaborate closely with engineering, product, and analytics teams to ensure reliable and high-quality data.
If you care about data as a product, enjoy solving complex data issues, and want to improve the reliability of data operations at scale, we’d love to talk.
ResponsibilitiesData Quality & Observability: Define and maintain data quality and observability requirements, metrics, and SLAs. Establish monitoring and controls to ensure data accuracy, completeness, consistency, timeliness, and overall reliability.
Monitoring & Alerting: Build and maintain monitoring, dashboards, and alerts to proactively identify anomalies, data drift, duplicates, missing or delayed data, and other quality issues.
Incident Investigation & Remediation: Own the data incident lifecycle end-to-end — from detection and initial assessment through investigation, root cause analysis, remediation, and preventive actions.
Data Lifecycle: Understand the key stages, from data ingestion and transfer to processing, transformation, storage, and consumption. Understand common issues that can occur at these stages and their potential impact on the quality of the resulting data.
Automation & Coverage: Expand automated data quality checks and operational controls to detect issues early, reduce manual work, and improve the reliability and coverage of data validation.
Cross-Functional Collaboration: Work closely with developers, product managers, analysts, and other teams to investigate data issues, clarify requirements, improve operational processes, and drive long-term quality improvements.
Documentation: Maintain and evolve documentation covering data quality and monitoring requirements, incident management processes, investigation findings, and approaches to preventing recurring issues.
Relevant Experience: 5+ years of experience in DataOps, Data Quality, Data Engineering, Data Analytics, or a related field, with strong practical experience in data operations and troubleshooting.
Data Quality & Observability: Strong understanding of data quality dimensions such as completeness, accuracy, consistency, timeliness, and validity, as well as data observability principles and monitoring approaches.
Quality Assurance Mindset (important): Strong understanding of quality assurance principles and hands-on experience applying them to data and data-related systems, including test design, data validation, edge-case identification, and systematic investigation of issues.
Incident Management: Strong understanding of the data incident lifecycle, including detection, triage, investigation, root cause analysis, remediation, and prevention of recurring issues.
Data Lifecycle & Pipelines: Good understanding of data lifecycles and data processing pipelines, including ETL, batch, and streaming approaches.
CLI & Linux: High comfort level working in the terminal and Linux environment, using command-line utilities to process and analyze text and tabular data, as well as reading and analyzing application and system logs.
Developer Workflows: Solid knowledge of JSON/YAML, version control (Git/GitHub), CI/CD practices (e.g., GitLab, Jenkins), and general SDLC concepts.
Automation & Scripting: Confident use of Python and Bash for data processing, incident investigation, and quality control automation. Confident use of regular expressions for searching, analyzing, and processing information.
Financial Domain: Strong understanding of financial data and financial markets. Experience with investments, trading, brokers, exchanges, market data, or products for analyzing financial markets, including TradingView, is highly valuable.
English: B2+ English proficiency, with strong reading and written technical communication skills.
Will be a plus
Advanced Data Stack: Experience with streaming platforms (e.g., Kafka), data observability tools, data catalogs, or metadata management, including OpenMetadata. Understanding of dbt.
Data Quality Automation: Experience building or maintaining data quality frameworks and automated validation systems.
Modern Tooling: Familiarity with AI tools and AI-assisted engineering workflows.
Financial Product Context: Familiarity with TradingView as a product or experience with similar products for analyzing financial markets.
Flexible working hours and a hybrid work format
Well-equipped offices for focused and collaborative work
A global, distributed team of 500+ professionals
Learning, mentorship, and long-term career growth
Relocation support and private health insurance
Performance-based bonuses
TradingView Premium access
Regular team events and company-wide meetups
Join the TradingView team and help us build a product used by millions of traders and investors worldwide. We look forward to hearing from you!
TradingView is an equal opportunity employer. We embrace diversity and are dedicated to fostering a diverse and inclusive workplace. Our success is driven by 600+ professionals from 40+ countries who speak nearly 20 languages.