Data Product Engineering Specialist - Python/SQL (5-10 yrs)

Responsibilities :

• Design, build, and maintain scalable data products to support analytics, AI/ML, and operational business needs.
• Develop high-quality data pipelines and reusable configurable frameworks, ensuring robust, efficient, and scalable data processing.
• Implement data transformation and enrichment processes to make raw data useful for pricing, risk modeling, claims, and other business functions.
• Ensure adherence to data modeling standards, reference data alignment, and data product governance frameworks.
• Work closely with Product Managers, Designers, and Domain SMEs to embed best practices and standards.
• Leverage cloud-based technologies and modern data platforms focusing on Azure data services and Databricks.
• Ensure solutions align with data security, privacy, and governance policies.
• Engage with platform teams to ensure efficient deployment and performance optimization of data solutions.
• Develop automated test routines and conduct thorough testing to ensure quality deliverables.
• Integrate solutions with data management tools such as Purview to automate data quality rules implementation, metadata management, and tagging.
• Develop clean and precise documentation, inc. low-level designs, release notes, and how-to guides.
• Support Data product launch and adoption activities.
• Keep up to date with new skills - Develop technology skills in other areas of Platform.
• Stay updated on emerging data technologies and continuously enhance data products to improve efficiency and business value.
• Optimize data processing performance and cost-efficiency by leveraging automation and modern engineering best practices.
• Identify opportunities for AI/ML integration and automation within data pipelines and business processes.

• Advanced understanding of building and deploying production-grade solutions in complex projects.
• Strong experience in data engineering, data modeling, and pipeline development.
• Proficiency in SQL, Python, Spark, Databricks, and cloud-based data engineering tools (Azure preferred, AWS, GCP).
• Strong experience in software engineering practices and implementation.
• Experience with big data processing, streaming architectures (Kafka, Event Hub), and real-time data solutions.
• Understanding of data governance, metadata management, and data lineage frameworks.
• Knowledge of CI/CD practices, DevOps, and Infrastructure as Code (IaC) for data solutions.
• Knowledge of using advanced tools and techniques to identify and address data quality issues.
• Strong stakeholder management, able to influence and drive alignment across cross-functional teams.
• Excellent problem-solving and analytical skills with a product mindset.
• Ability to work in agile environments, contributing to product roadmaps and iterative development cycles.