• Own the end-to-end architecture and roadmap for Montrose’s enterprise data and AI platform on Snowflake (Azure) • Lead the transition from legacy .NET and MS SQL–based data environments to modern cloud-native data patterns • Define and enforce standards for data ingestion, transformation, modeling, analytics, and AI enablement • Establish modern ELT pipelines using dbt, Fivetran, and/or appropriate Azure-native equivalents • Design the platform to be Data Cloud–ready, supporting future integrations with Salesforce Snowflake Data Cloud and Workday Snowflake Data Cloud • Lead the evaluation, selection, and implementation of enterprise BI and analytics tooling • Lead the evaluation and adoption of enterprise AI / LLM platforms (e.g., Abacus or comparable solutions) • Design and deliver an initial AI-driven use case that automates and materially improves testing, monitoring, and remediation workflows • Ensure the initial AI use case delivers measurable efficiency, reliability, or cost improvements that justify capital investment • Establish analytics-ready and AI-ready data models • Define data and AI governance practices including security, access control, lineage, and cost management • Serve as the senior technical authority for data, analytics, and AI platform decisions • Partner with IT, Engineering, Operations, Finance, and executive leadership • Build and lead the data organization, starting with a senior data engineer • Leverage a nearshore delivery model utilizing Latin American talent • Mentor engineers and analysts, setting expectations for rigor, documentation, and measurable outcomes

• Responsible for the company's Data department • Oversee data studies and analyses • Ensure compliance with agreed deadlines • Team development • Improve the product with a data-driven perspective • Understand the importance and urgency of client requests • Monitor prioritization progress • Propose improvements to outcomes • Organize the department's workflows • Participate in internal and external company alignments • Study and understand new types of fraud to generate insights and propose improvements

• Develop end-to-end AI solutions from discovery through deployment and monitoring, including modeling (NLP/ML/LLMs), APIs and integrations. • Design and evolve chatbots and conversational channels using NLP/NLU, driving continuous improvement. • Build integrations via APIs/JSON, microservices and event-driven architectures, and implement MLOps with pipelines, versioning and observability. • Analyze conversational data and recommend actions based on KPIs, ensuring quality through testing and validation. • Work in agile methodologies, collaborating with business, design, engineering and security teams.

• Steward core data assets: Maintain and steward the data that powers Atlas. Contribute to pipelines and infrastructure alongside engineers, and own data quality and integrity long-term. • Design data models: Develop and maintain foundational data models, tables, metrics, and transformations that serve as source of truth for the data product. • Be the data expert: Develop deep expertise in the civic data that powers Atlas, including metadata and data contracts. Understand what partners and internal teams need, and find ways to make data more accurate, accessible, and usable. • Support data consumers: Help researchers, product owners, and other downstream consumers understand data logic and how data flows through Atlas. Build relationships across teams to ensure collaborative problem-solving. • Resolve data issues: Investigate and fix issues when they arise, coordinating with internal stakeholders and external parties (e.g., data vendors, technology partners, etc.). • Improve operations: Proactively identify opportunities to enhance data quality, coverage, or timeliness. Find ways to make data processes more efficient and implement improvements to tools and methodologies.