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We 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.
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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.
TradingView is a private financial charting platform and social network serving traders and investors worldwide.
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