Responsibilities
- Formulate strategic data architecture frameworks and propose technical solutions aligned with organizational objectives.
- Manage end-to-end data modeling processes, including conceptual design, logical modeling, physical implementation, and performance tuning, including SQL coding and database management.
- Evaluate business data needs, investigate available data assets, and document functional requirements.
- Collaborate on the creation of new data pipelines and integrations, ensuring alignment with current data warehouse standards.
- Build efficient ETL workflows, analyze system logs, and optimize data processing for scalability and functionality.
- Implement validation and auditing mechanisms to maintain data accuracy and consistency across systems.
- Lead or support the development of business intelligence presentation layers and semantic models.
- Create, test, and deploy interactive dashboards, performance scorecards, reports, and alert systems based on user needs and aligned with BI and data warehouse architecture.
- Produce technical and user documentation for BI tools and deliver training and ongoing support to end users.
- Lead the creation of high-level conceptual and logical data models that support enterprise-wide data integration.
- Supervise the mapping of data flows, interfaces, and analytics pipelines to uphold data quality standards.
- Define and enforce data quality protocols, including methods for monitoring completeness, consistency, redundancy, and improvement.
- Perform data capacity assessments, lifecycle planning, retention policies, usage projections, and feasibility evaluations.
- Partner with project managers and business leaders on enterprise data initiatives and cross-functional projects.
- Identify strategic opportunities for data reuse, archiving, or decommissioning of legacy systems.
- Offer expert guidance during business requirement sessions to ensure data feasibility and alignment.
- Support departments in designing and building subject-specific data marts as needed.
- Design and deploy scalable data warehouse architectures, including high-efficiency ETL tools, processes, and infrastructure.
- Lead data profiling and quality assessment activities using SQL and query-based techniques.
- Develop and apply data cleansing methodologies to correct inaccuracies and standardize datasets.
- Identify and integrate internal and external data sources to expand analytical capabilities.
- Reverse engineer data structures from operational systems using discovery and analysis methods to document specifications.
