• Provide leadership of the Performance Team, ensuring effective use of Black Diamond for data aggregation, performance reporting, and regulatory reporting. • Design, build, and maintain ETL processes and SQL pipelines that power enterprise reporting and analytics. • Develop, optimize, and document SQL Server stored procedures, views, and data transformations. • Ensure high-quality, well-structured, and auditable data across Choreo’s production systems. • Support data conversions and integration efforts during M&A onboarding projects. • Enhance existing Power BI datasets, reports, and dashboards based on advisor and leadership feedback. • Improve data models for scalability, performance, and ease of maintenance. • Partner with business teams to translate requirements into meaningful analytics solutions. • Build and maintain low-code automations and integrations using Power Automate and Power Apps. • Explore and experiment with emerging AI tools and capabilities (e.g., Copilot Studio, ChatGPT) to streamline processes. • Support internal innovation projects designed to streamline workflows and improve the client experience.

• Integrate and Scale: Extend, scale, monitor, and manage our ever-growing pile of 100s of integrations. Ensure they are reliable and responsive. • Own the Integrations: Implement new connections and be the master and owner of these integrations. • Optimize AI Pipelines: Ensure our AI pipeline works like clockwork. Improve and support its complex infrastructure. • ML Experience: Practical experience with ML, including classification, clustering, time series forecasting, and anomaly detection. You need to know the concept and be handly with the most common libraries. • Model Hosting and Monitoring: Host and monitor NLP models and LLM for real-time and batch inference. • Develop Monitoring Systems: Create and support robust models for monitoring. • Support and Innovate: Assist with a long tail of super important tasks, bringing innovative solutions to the table.

• Own & Evolve the Benepass Data Platform • Serve as the primary architect and builder of our data infrastructure across our AWS stack (RDS, Redshift, S3, etc.). • Evaluate and lead the evolution of our data stack, including decisions around warehousing, orchestration, transformations, ingestion, and observability. • Define ingestion, modeling, testing, and monitoring frameworks to ensure data is accurate, reliable, and easy to use. • Partner with Engineering, Product, Finance, People, Sales, Operations, and the Executive Team to understand data needs and deliver high-value pipelines, models, and datasets. • Support financial and people analytics, revenue and margin reporting, operational workflows, and compliance/audit needs. • Provide tooling, infrastructure, and best practices to empower the Operations Data team and other internal data consumers. • Design and implement ETL/ELT pipelines, schemas, and transformations in our AWS environment. • Debug complex data issues, especially those spanning payments, benefits, and financial systems. • Establish standards for data reliability, testing, documentation, privacy, and governance. • Create clarity where none exists—define architectural direction, roadmap, SLAs, data contracts, and cross-team processes. • Anticipate scaling needs and prepare the platform for increased volume, complexity, and product usage. • Influence product and engineering teams on event instrumentation, schema design, and logging to ensure downstream data is structured and reliable. • Define the future data engineering team structure, establish foundational patterns, tooling, and documentation, and position Benepass to scale efficiently once we do grow the team.

• Drive data architecture unification across multiple SaaS products, creating coherent patterns while respecting product needs. • Partner with vertical technical leads to provide horizontal architectural support and alignment. • Design and optimize data storage across multiple technologies—AWS OpenSearch/Elasticsearch, relational databases, S3, NoSQL, and data warehousing. • Optimize for performance and resilience— indexing, querying, high availability, redundancy, and disaster recovery at scale. • Support AI initiatives—partner with our AI specialist team on data architecture for AI capabilities. • Bring clarity from ambiguity—translate complex challenges into clear architectural direction. • Build capability, not dependencies—mentor engineers so teams become more self-sufficient. • Balance performance, cost, and reliability across our platform.