In our “Engineering Energizers” Q&A series, we highlight engineering leaders who are shaping the future of technology. Today, we feature Lior Ebel, Principal Architect of Software Engineering at Salesforce, who leads the architecture of Data Cloud’s semantic layer, Tableau Semantics. This advanced engine powers Tableau Einstein, Salesforce’s premier analytics platform, enabling businesses to query and model data with unparalleled efficiency and accuracy.
Discover how the team addresses challenges including optimizing large-scale semantic queries, integrating AI for intuitive data modeling, and ensuring seamless scalability without compromising trust and security—all contributing to smarter, data-driven decision-making across the Salesforce ecosystem.
What is your team’s mission?
Our team empowers businesses with a unified and intuitive way to model and analyze their data. We focus on advancing Tableau Semantics, the core data querying and modeling engine for Tableau Einstein. Our primary goal is to simplify how organizations define data relationships and business metrics, creating a single source of truth for consistent and reliable insights.
Through Semantic Data Models (SDMs), users can structure their data for high-level, intuitive queries. This approach reduces the complexity of traditional SQL and accelerates the development of data-driven applications. The semantic layer query engine translates these queries into efficient SQL based on SDM definitions.
Here are some high-level examples of what such a translation may look like:
Leveraging cutting-edge technology like generative AI, we integrate AI-driven agents and natural language capabilities. This enables users to query data conversationally without needing technical expertise. Our ultimate goal is to make data accessible, actionable, and valuable while ensuring a seamless, intelligent, and scalable analytics experience.
What was the most significant technical challenge your team faced recently?
One of our most significant technical challenges was ensuring that semantic layer queries perform efficiently at a B2C scale, where vast amounts of data must be processed quickly and accurately.
To address this, the team chose a SQL generator-based approach for the semantic layer query execution engine instead of building our own query processor. This strategic decision allows us to leverage the scalability and optimization features of underlying data warehouses, avoiding the need to reinvent performance tuning.
We also emphasize optimizing the SQL queries generated by the semantic layer. By using efficient joins, applying early filtering, and minimizing query complexity, we follow best practices in SQL design. Additionally, we test and refine SQL patterns early in development, proactively identifying and resolving performance bottlenecks.
This dual focus on architecture and SQL optimization enables us to handle large-scale B2C data, delivering fast, reliable, and scalable analytics.
How do you manage challenges related to scalability?
Scalability presents several complex challenges, including growing data volumes, increasing customer usage, rising query loads, and expanding teams interacting with the system. Each of these requires targeted strategies to address effectively.
To manage system load, we employ a two-pronged approach: top-down and bottom-up. During the design and planning phases, we identify and mitigate potential bottlenecks through collaboration and by leveraging our team’s expertise. This proactive top-down strategy is complemented by stress-testing during development. Simulating high loads helps us uncover bottlenecks that may not have been evident earlier, ensuring we’re ready for unforeseen scaling challenges.
To support the growing number of teams interacting with the system, we prioritize stability and usability. Thorough documentation, intuitive APIs, and a self-service design enable both internal and external teams to scale their usage independently. This reduces the need for direct support, allowing for seamless scaling while maintaining performance and usability standards.
What ongoing research and development efforts are aimed at improving your project’s capabilities?
The team has been focused on integrating AI into the semantic layer to create more intelligent and scalable data experiences within Salesforce. Over the past year, our efforts have centered on leveraging Semantic Data Models (SDMs) to support AI-driven agents and co-pilot experiences. By grounding these AI tools in an organization’s specific business definitions, we ensure that responses to data queries are consistent, accurate, and aligned with unique metrics, thereby reducing issues like LLM hallucinations.
AI is also being utilized to improve the process of creating and refining semantic models. AI-powered assistants can suggest best practices for defining business logic and recommend efficient data model structures, streamlining SDM development as complexity increases. As an example, our AI assistant may suggest a formula for calculating your ROI in response to free text request like “create an ROI field please”.
Positioning the semantic layer as an AI-powered foundation enables smarter decision-making. Advancing these capabilities ensures reliable, intuitive experiences across the Salesforce ecosystem, empowering businesses to make data-driven decisions with greater confidence.
What strategies did your team employ to ensure enhancements in one area of your project did not compromise others?
In software development, trade-offs are inevitable, so it’s crucial to ensure that enhancements in one area don’t create issues elsewhere. A key strategy our team employs is fostering open communication and collaborative decision-making. By involving all relevant perspectives before making decisions, we ensure that no aspect of the project is overlooked.
Brainstorming sessions and cross-functional discussions are integral to this process. These forums allow team members from various roles to identify potential implications and provide valuable feedback. We encourage both verbal and written input to ensure that all concerns are heard and ideas are shared.
This emphasis on transparency and collaboration minimizes the risk of creating silos and helps the team make well-rounded decisions. Our team is spread across several geo-locations, which brings a lot of value and diverse thoughts but also requires proactive investment and thought in the collaboration process. By balancing the needs of the entire project, this approach ensures that improvements are implemented without unintended compromises—a strategy that has consistently proven effective for us.
How do you balance the need for your project’s rapid deployment with maintaining high standards of trust and security?
Balancing rapid deployment with high standards of trust and security is a core priority, especially given the nature of Salesforce’s customer base. We integrate trust and security into every step of the development process, supported by Salesforce’s robust foundational infrastructure. This foundation allows us to deploy quickly without compromising the critical elements of security and trust.
The semantic layer is built on Hyperforce, which abstracts much of the complexity around security and compliance. By leveraging this infrastructure, we avoid reinventing security measures, enabling both speed and reliability. A solid infrastructure forms the backbone of maintaining high security standards.
Beyond infrastructure, rigorous quality assurance and validation processes are crucial. Every release undergoes extensive testing to ensure it meets the highest standards. This combination of trusted infrastructure and diligent testing allows for rapid development while keeping security and customer trust as top priorities.
How do you gather feedback from users and how does it influence future development?
The semantic layer serves two primary user groups: external users who interact with it as a product, and internal users—Salesforce developers who use it in their workflows.
For external users, the product team engages in ongoing conversations with key stakeholders to gather feedback and address pressing pain points. This ensures the product aligns with user needs.
Internal users (e.g. business Intelligence app developers) provide feedback through open communication channels, such as Slack for asynchronous input and face-to-face meetings as needed. Software development is inherently human-centered, so understanding the challenges faced by both developers and end users is crucial for building effective solutions. Often, the most compelling technical challenges come directly from user pain points, as these represent problems users can’t solve on their own.
Development priorities are heavily influenced by user feedback to enhance the product’s real-world utility. However, proactive innovation is also critical, as it allows us to anticipate future needs and push boundaries beyond existing requests.
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