Amazon Web Services (AWS), Backlog Prioritization, Business Skills, Data Analysis, Data Management, Data Quality, Data Warehousing, Database Extract Transform and Load (ETL), Design Patterns Programming Methodologies, Finance, GCP (Good Clinical Practices), High Availability, Microsoft Windows Azure, Process Improvement, Product Engineering, Product Management, Product Planning, Quality Management, Requirements Management, Risk, Root Cause Analysis, Sales, Snowflake Schema, Sprint Planning, Supply Chain, Sustainability, Validation Testing
Mandatory Skills:
• Strong understanding of Data as a Product mindset
• Ability to convert business outcomes into data product requirements
• Defining and managing Data product scope/Data contracts
• Domain knowledge (e.g., Finance, Sales, Supply Chain, Customer, Risk)
• Expert in ETL / ELT design patterns
• Hands on experience with Azure / AWS / GCP
• Hands on experience with Data Warehouses (Snowflake/Azure Synapse)
Data Product Engineering Ownership
• Lead the design, build, and maintenance of domain owned data products
• Translate product requirements into scalable data engineering solutions
• Ensure data products meet defined:
o Functional requirements
o SLAs / SLOs
o Quality and compliance standards
Collaboration with Data Product Owner
• Partner closely with the Data Product Owner to:
o Understand business outcomes and priorities
o Refine data product scope and roadmap
o Balance delivery speed with technical sustainability
• Provide technical input for backlog prioritization and sprint planning
Data Pipeline & Transformation Design
• Design and implement ETL/ELT pipelines for data products
• Support:
o Batch and near real time processing
o Structured and semi structured data
• Manage schema evolution and backward compatibility
• Implement data contracts for product consumers
Data Quality, Reliability & Trust
• Define and enforce data quality rules for data products
• Implement:
o Validation checks
o Reconciliation logic
o Data freshness monitoring
• Ensure high availability and fault tolerance
• Lead root cause analysis for data incidents
• Drive continuous quality improvement