• Develop and deliver innovative solutions aligning with the organization’s goals • Maintain scalable, reliable, consistent, and repeatable systems for company-wide analytics • Design, build, and maintain real-time and batch data pipelines • Ensure data quality, observability, validation, and governance • Collaborate with business, IT, and analytics teams to translate needs into technical solutions • Support integration with enterprise applications like Oracle ERP, Anaplan, Salesforce, and Tableau • Create and maintain technical documentation

• Design, develop, and maintain scalable ETL/ELT pipelines to process large volumes of data from multiple sources. • Build and optimize data lakes / data warehouses / lakehouse structures for efficient reporting and analytics. • Integrate structured and unstructured data from internal and external systems to create a unified and analysis-ready layer. • Ensure data accuracy, consistency, and completeness through validation, cleansing, transformation, and reconciliation checks. • Maintain strong documentation for data pipelines, datasets, Power BI models, and reporting logic. • Develop and maintain Power BI dashboards and reports for business and leadership stakeholders. • Build optimized semantic models in Power BI (star/snowflake schema) for performance and scalability. • Write and optimize DAX measures, calculated columns, and KPIs aligned to business metrics. • Use Power Query (M) for transformations and ensure reporting datasets are refresh-ready and reliable. • Manage Power BI deployments via Power BI Service, including scheduled refresh, workspace access, and report sharing. • Improve dashboard performance by optimizing data model design, relationships, and aggregations. • Collaborate with product managers and business stakeholders to gather reporting requirements and translate them into technical deliverables. • Work in Agile development cycles including sprint planning, daily stand-ups, and sprint reviews. • Perform thorough testing of pipelines and reports to ensure reliability, performance, and data correctness.

• Designing enterprise information architecture under limited direction to store, move, transform, deliver, integrate, and manage data. • Contributing to the growth of the Data Architecture practice by identifying common patterns and developing frameworks for all data management disciplines. • Executing the data strategy and vision in alignment with business goals by utilizing the modeling standards, guidelines, best practices, and approved modeling techniques. • Modeling the future state of the data architecture and provides best practices and guidelines. • Reviewing proposed data models in packaged or commercial applications for business applicability. • Contributing significantly to data integration, business intelligence (BI), and enterprise information management programs. • Establishing metrics to track and measure the value of data architecture initiatives (e.g., model reuse, project cost impact, data consistency improvements). • Identifying significant data assets based on business impact, decision impact, risk mitigation, or organizational relevance. • Ensuring critical information assets are represented in the enterprise information architecture (EIA) and comply with its design guidelines.

• Collaborate with product managers, data scientists, analysts, and engineers to define business requirements and translate them into scalable data solutions. • Plan, design, build, test, and deploy data warehouse, data mart, and ETL/ELT pipelines—primarily within Google Cloud and BigQuery. • Lead small to medium data projects, including scoping, design, documentation, and execution. • Architect and operate cloud-based data pipelines that support reporting, invoicing, and analytics use cases. • Ensure data integrity, consistency, and accessibility across internal and external data products. • Develop, document, and enforce coding and data modeling standards to improve code quality, maintainability, and system performance. • Serve as an in-house data expert, making recommendations on data design, pipeline improvements, and best practices. • Build and maintain: Internal dashboards and reporting packages (weekly, monthly operational and financial reporting). • External reporting packages for customers, including standardized QBRs and trend analyses. • Monthly invoicing logic, automated data workflows, and standardized datasets for finance. • Work cross-functionally to expand usage and value of the data warehouse, promoting a data-driven culture across the organization.