Senior Data Product Manager
- תל אביב
- משרה קבועה
- משרה מלאה
- Own the vision, roadmap, and delivery of critical datasets that underpin platform functionality, decisioning systems, and customer-facing features.
- Lead modularization of data structures to enable scalability, easier ownership distribution, and data quality improvements.
- Introduce metrics for quality (completeness, coverage, freshness, accuracy) and define validation, logging, and governance frameworks.
- Serve as the product voice for data across R&D, aligning platform engineers, analysts, scientists, and business teams around a common strategy.
- Bridge between domain-specific product teams and platform data infrastructure, translating business needs into scalable, reusable data assets.
- Define data SLAs, fallback mechanisms, enrichment strategies, and quality assurance mechanisms in partnership with data consumers.
- Prioritize and plan data initiatives that support new verticals, geographies, product features, and business models.
- Spearhead productization of internal datasets into revenue-generating assets for both direct sales and platform distribution.
- Deeply understand the customer's evolving needs and enable the data to capture them accurately and scalably.
- Write clear product specs that include technical definitions, mapping rules, edge case handling, and downstream consumption logic.
- Work hands-on with Databricks, Spark, SQL, and Python to analyze datasets, test logic, or collaborate on experiments.
- Identify data anomalies, misclassifications, and inconsistencies, and lead efforts to resolve them in systematic and productized ways.
- Design workflows for historical tracking and versioning of datasets.
- 4+ years of experience in data product management, or as a technical product manager or solutions architect in a big data environment.
- Proven track record in leading cross-functional data initiatives involving data engineering, data science, and analytics teams.
- Experience delivering data products for both revenue-generating direct sales use cases and scalable platform integration.
- Hands-on experience with large-scale data collection, classification, curation, and delivery systems, especially in companies that build or sell data products.
- Experience driving data modeling and transformation projects with complex entity relationships and high-quality standards.
- Practical exposure to AI/ML productization: partnering with data scientists to define training data, evaluate model performance, and integrate AI outputs into production systems.
- Strong analytical and systems-thinking mindset with deep understanding of how data flows and scales.
- Ability to define validation frameworks, QA rules, and performance metrics for structured and semi-structured data.
- Excellent communicator who can translate between technical and business stakeholders and align diverse interests into unified outcomes.
- Collaborative leadership style with a bias for action and ability to resolve ambiguity through structure and logic.
- Comfortable querying large datasets and working alongside large scale data pipelines.
- Familiarity with Python, Spark, Airflow, and cloud data environments (e.g., Databricks, S3, Snowflake) is a strong plus.
- Understanding of AI/ML lifecycle, including model inputs, inference pipelines, and continuous data feedback loops.