Description

Responsibilities:

• Design enterprise data warehouse solutions that integrate diverse data sources and support high performance analytics, ensuring reliable and consistent information for critical business decisions.
• Develop comprehensive data architecture blueprints that define data flows, integration patterns, and storage strategies, aligning with long term organizational goals and technology standards.
• Create logical and physical data models that optimize structures for analytical workloads, enabling efficient querying, reporting, and advanced data exploration across subject areas.
• Define data best practices and governance standards including validation approaches to create, monitor, and maintain data quality.
• Create SQL queries, analyze the results, and review and resolve any concerns with dealers to validate the data between source and target databases to ensure data quality.
• Execute maturity assessments of the data quality on a defined frequency.
• Identify missing or incorrect data and data standards.
• Work at an enterprise level of interaction with responsibilities of providing data to multiple consuming systems where the actions taken must be very well defined to not impact revenue generating business activities or manage data volumes at extremely high levels where the process and quality are more complex due to the nature of the volume or data.
• Participate in requirements for any technical implementations or enhancements to the data management systems.
• Create metrics reporting on data management activities as defined.
• Monitor and act on any workflow requests for new, changed, or removal of Implement data integration patterns using industry standard tools and frameworks to enable secure, accurate, and timely data movement across systems in the hybrid work environment.
• Define and enforce data governance practices that address data ownership, stewardship, lineage, and usage, helping the organization maintain trusted and well documented data assets.
• Establish data quality rules, validation checks, and monitoring processes to identify anomalies, improve accuracy, and increase confidence in analytical outputs for decision makers.
• Ensure data architecture complies with regulatory and internal compliance requirements by embedding privacy, security, and retention controls throughout data pipelines and storage layers.
• Guide teams on reference architectures, design standards, and best practices for data warehousing and modeling, supporting consistent implementation across projects without formal people management responsibilities.
• Optimize data warehouse performance through partitioning, indexing, and query tuning strategies that reduce latency and improve responsiveness for business intelligence and self service analytics.
• Coordinate with infrastructure and platform teams to plan capacity, scalability, and resilience of data platforms, ensuring reliable operation under changing workloads and growth demands.
• Document data models, metadata, and architectural decisions in a clear and accessible manner, enabling future maintainability and simplifying onboarding for technical and analytical teams.
• Partner with analytics, reporting, and data science teams to design curated data sets and semantic layers that accelerate insights and foster responsible data use across the organization.

Requirements:

• Experience: 9 to 12 Yrs
• Required Skills: Data Modeler, Data Architect, Data Quality Basics, Data Governance & Compliance, Data warehousing
• Possess extensive experience in designing and implementing enterprise data warehouses with a strong record of delivering stable, scalable, and maintainable platforms for analytics.
• Demonstrate advanced expertise as a data modeler with hands on practice in conceptual, logical, and physical modeling for both normalized and dimensional structures.
• Exhibit proven capabilities as a data architect in defining end to end data solutions that integrate multiple source systems and support complex analytical and reporting needs.
• Apply solid understanding of data governance and compliance principles to create architectures that respect privacy, regulatory expectations, and internal risk controls.
• Show practical knowledge of data quality basics by defining rules, metrics, and remediation approaches that improve the reliability of key data elements over time.
• Communicate effectively in English through clear written documentation and confident verbal discussions with technical and non technical stakeholders in a hybrid work setting.
• Bring nine to twelve years of progressive experience working in data warehousing, data architecture, or related roles within complex or multinational environments.
• Display familiarity with modern data warehousing practices on cloud or on premises platforms and adapt designs to evolving tools, frameworks, and organizational priorities.
• with infrastructure and platform teams to plan capacity, scalability, and resilience of data platforms, ensuring reliable operation under changing workloads and growth demands.
• Document data models, metadata, and architectural decisions in a clear and accessible manner, enabling future maintainability and simplifying onboarding for technical and analytical teams.
• Partner with analytics, reporting, and data science teams to design curated data sets and semantic layers that accelerate insights and foster responsible data use across the organization

Key Skills

Education

Any Graduate