Senior Data Engineer

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🕒 February 19

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Qualifinds

WebsiteLinkedInAll Job Openings

11 - 50 employees

🎯 Recruiter

👥 HR Tech

Recruitment • HR Tech

Qualifinds is a specialized recruitment company that focuses on optimizing the hiring process by integrating subject matter experts (SMEs) into the evaluation of candidates. The company aims to connect businesses with top talent efficiently, ensuring candidates are not only a great fit for the company culture but also possess the necessary technical knowledge for their roles. By providing comprehensive visibility into the hiring process, Qualifinds helps businesses organize and synchronize their recruitment efforts, allowing for intelligent decision-making and rapid growth.

📋 Description

• Architect and scale the systems that power AI-driven valuation tools, market analytics, and collector insights • Lead development of data ingestion pipelines, enrichment workflows, and metadata infrastructure • Design and operate scalable data ingestion and web scraping systems • Build batch and real-time pipelines to normalize, enrich, and version data across structured and unstructured sources • Develop systems to support LLM- and ML-based document parsing, OCR, and classification • Own the architecture of data storage and processing stack, including PostgreSQL, data lakes, and data warehouses • Operationalize AI/ML workflows by preparing clean training and inference datasets with robust lineage, validation, and error handling • Integrate output with backend APIs, valuation services, and frontend analytics dashboards • Collaborate across engineering and product to ship reliable, intelligent features quickly • Contribute to infrastructure tooling, including CI/CD, IaC (Terraform), and data observability

🎯 Requirements

• B.S in Computer Science or equivalent • 5+ years of experience building data platforms in production environments, ideally in startup or fast-moving contexts • Fluent in Python, SQL, and familiar with orchestration tools like Airflow, Dagster, or Temporal • Have designed or maintained web scraping pipelines at scale, using best practices around retries, proxies, and anti-bot strategies • Understand tradeoffs between ETL and ELT, and have hands-on experience with data lakes and data warehouses • Have integrated structured and unstructured sources, and enjoy resolving messy edge cases that come with real-world data • Have worked with LLM APIs or ML models in production, particularly for document understanding, NLP, or entity extraction • Thrive in AI-native environments and enjoy building tools that support intelligent automation and analytics • Care about clean architecture, versioning, reproducibility, and quality in data systems

🏖️ Benefits

• Meaningful equity • Competitive compensation • Remote-flexible culture