Data Architecture with Salesforce

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Job Purpose

Lead the design and implementation of enterprise data architecture and reporting solutions that enable scalable, high-performance data environments. This role focuses on building robust data models, ETL pipelines, and reporting frameworks across modern data platforms (e.g., Snowflake, Databricks, Tableau, Power BI) to improve data accessibility, reporting accuracy, and decision-making. The objective is to deliver actionable insights, strengthen data governance, and support sales and distribution functions with reliable, timely, and transparent data.

Enterprise Data Architecture & Strategy Define and implement enterprise data architecture, including logical and physical data models, aligned with business and technology strategies. Design scalable and high-performance data solutions to support analytics, reporting, and operational needs. Establish standards and best practices for data modeling, integration, and platform usage. 2. Data Engineering & ETL Development Architect and oversee development of ETL/ELT pipelines for ingesting, transforming, and loading data from multiple source systems. Ensure efficient source-to-target mappings and data transformations across enterprise systems. Optimize data pipelines for performance, scalability, and reliability using platforms such as Snowflake and Databricks. 3. Data Integration & Platform Enablement Lead integration of disparate data sources including CRM systems, distribution platforms, and external data providers into a unified data ecosystem. Collaborate with engineering teams to implement batch and real-time data ingestion frameworks. Enable seamless data availability for reporting, analytics, and downstream applications. 4. Reporting & Analytics Frameworks Design and implement reporting frameworks and dashboards using tools such as Tableau, Power BI, and CRM Analytics. Deliver standardized, scalable reporting solutions tailored to sales, distribution, and business stakeholders. Ensure consistency in KPIs, metrics definitions, and reporting across the enterprise. 5. Data Governance & Quality Management Establish and enforce data governance frameworks, including data quality controls, metadata management, and lineage tracking. Define data standards, validation rules, and monitoring processes to ensure accuracy and integrity. Partner with governance teams to ensure compliance with regulatory and organizational policies. 6. Performance Optimization & Operational Efficiency Continuously monitor and optimize data platforms and reporting systems for performance and cost efficiency. Implement best practices in data partitioning, indexing, and query optimization. Improve operational efficiency through automation and streamlined data processes.