Director Data Services

• Data Platform & Engineering Leadership
  • Own the strategy, architecture, and performance of Lexipol's enterprise data platforms, including data pipelines, warehousing, and integration infrastructure supporting both internal operations and customer-facing products.
  • Ensure data systems are scalable, reliable, secure, and aligned with business priorities across revenue, finance, marketing, operations, and product functions.
  • Establish governance and decision frameworks for selecting, implementing, optimizing, and sunsetting data platforms and services.
  • Oversee the design and maintenance of data pipelines and integrations that ensure accurate, timely, and consistent data flows across internal business systems and product environments.
  • Partner with the Engineering, Solutions Management, and Head of AI to ensure data infrastructure supports AI/ML initiatives and responsible adoption of AI-enabled capabilities.
• Product Data & Software Development Lifecycle
  • Serve as the data organization's primary partner to Solutions Management and Engineering in delivering product roadmap initiatives that include reporting, analytics, and data-driven features for Lexipol's customers.
  • Ensure the data team is a fully integrated participant in the software development lifecycle, operating in sprints and contributing to roadmap planning, sprint execution, and release cycles alongside product and engineering teams.
  • Lead the delivery of customer-facing reporting, embedded analytics, and data products that are reliable, performant, and aligned with customer needs.
  • Balance internal data service commitments with product roadmap obligations, ensuring the team maintains high execution standards across both workstreams.
  • Partner with Solutions Management to translate customer and business requirements into data architecture decisions, reporting capabilities, and scalable product data solutions.
• Data Governance & Quality
  • Own enterprise data governance standards, data quality controls, and master data management practices across the organization.
  • Define and enforce policies for data access, data classification, retention, and stewardship across all internal platforms and product data environments.
  • Lead initiatives related to data cleanup, deduplication, lineage documentation, and ongoing data integrity management.
  • Serve as the organizational authority on data definitions, standards, and governance frameworks that support consistent reporting and decision-making for both internal and external stakeholders.
• Analytics & Business Intelligence
  • Oversee the development and maintenance of reporting infrastructure, dashboards, and self-service analytics capabilities that serve internal business stakeholders and external customers.
  • Partner with Revenue, Finance, Marketing, Customer Success, Operations, and Solutions Management to ensure data products and insights effectively support critical business processes and customer outcomes.
  • Define KPIs and reporting frameworks that measure operational performance and business outcomes.
  • Ensure leadership has access to timely, accurate, and actionable insights to support strategic and operational decision-making.
• AI Enablement & AI-Ready Data Infrastructure
  • Own the strategy for ensuring Lexipol's data lake and warehouse environments (AWS, Redshift) are architected, governed, and maintained in a way that makes data optimally structured, clean, and accessible for AI and machine learning consumption.
  • Define and enforce standards for data organization, metadata management, schema design, and documentation within the data lake and warehouse that support reliable AI ingestion, model training, and inference workflows.
  • Partner with the Head of AI and Engineering to deliver AI-powered capabilities — both within internal tools and customer-facing products — that enable internal stakeholders and customers to surface deeper insights from Lexipol's data.
  • Lead the team's use of AI in the development process itself, including the use of AI-assisted tooling to accelerate data engineering, pipeline development, analytics delivery, and documentation.
  • Evaluate and guide the adoption of AI-driven data tools and capabilities, ensuring responsible implementation aligned with governance, security, and compliance standards.
  • Serve as a key partner in defining what "AI-ready data" means at Lexipol — driving the practices, infrastructure decisions, and governance standards that make AI initiatives possible and scalable across the organization and its products.
  • Identify and prioritize opportunities where AI can help internal and external customers get more value from Lexipol's data — through natural language querying, intelligent reporting, automated insights, anomaly detection, and similar capabilities.
• Technology Investment & Vendor Strategy
  • Partner with Finance and DevOps to optimize data technology spend and maximize return on investment across the data stack.
• Compliance, Security & Audit Support
  • Ensure data systems and practices meet security, privacy, and compliance obligations, including support of the organization's annual SOC 2 audit.
  • Establish controls and processes to maintain audit readiness and ensure ongoing adherence to regulatory and governance standards across both internal and product data environments.
  • Maintain data access controls, role-based permissions, and documentation standards that support compliance and security requirements.
• Leadership & Cross-Functional Partnership
  • Lead and develop a team of approximately 6–10 data professionals, including data engineers, analysts, and platform administrators operating in an agile, sprint-based model.
  • Serve as a strategic partner to executive leadership on data investment decisions, platform strategy, and governance.
  • Act as the primary liaison between business stakeholders, Solutions Management, Engineering, IT, and external technology vendors on data-related initiatives.
  • Lead change management efforts during major data platform implementations, migrations, and organizational transitions.
  • Foster a team culture that balances operational rigor with the delivery cadence and collaborative norms of a product engineering organization.
  • Establish and monitor a set of KPIs to measure the performance of the Data Services organization and performance of the Data Lake.

• Deep experience designing and governing data lake and warehouse environments (ideally on AWS and Redshift) with AI ingestion and consumption in mind — including schema design, metadata management, and data organization best practices that enable AI/ML workloads.
• Ownership of enterprise data platforms, including data lakes, data warehousing, pipeline development, and analytics infrastructure serving both internal and external stakeholders.
• 10+ Years experience leading data teams that operate within or alongside software development lifecycles, including agile and sprint-based delivery models.
• Proven ability to partner with Product Management and Engineering to deliver customer-facing data and reporting capabilities as part of a product roadmap.
• Strong strategic and operational leadership with the ability to translate business and product needs into scalable data solutions.
• Proven success establishing data governance frameworks and driving data quality improvements across complex organizations.
• Experience building and delivering analytics and BI capabilities (ideally on PowerBI) that support both executive decision-making and customer-facing reporting.
• Ability to lead complex cross-functional initiatives and influence senior stakeholders across both business and technology organizations.
• Proven experience leading large-scale data platform implementations, migrations, or transformations that impact multiple departments and core business operations.
• Experience evaluating, implementing, or governing AI-enabled capabilities within enterprise data ecosystems, including customer-facing AI-driven analytics or insights features.
• Demonstrated ability to use AI tools in the development and delivery of data products, and to lead teams in responsible, effective AI-assisted workflows.
• Strong familiarity with modern AI productivity tools and a demonstrated ability to incorporate them into leadership, analysis, and operational workflows while guiding teams in responsible AI adoption.

• Bachelor's degree in Information Systems, Computer Science, Data Science, Business Analytics, or a related field (or equivalent experience).
• Extensive experience leading enterprise data platforms, analytics, or data engineering functions in a complex organization.
• Strong understanding of modern data stack components, including data warehousing, ETL/ELT pipelines, and BI reporting tools.
• Experience working within agile or sprint-based delivery models, ideally in organizations where data functions contribute directly to product development.
• Experience leading data initiatives through periods of growth, change, or acquisition.
• Experience overseeing compliance or audit processes such as SOC 2 as it relates to data systems and controls.
• Experience operating within SaaS or subscription-based business models and supporting the data systems that enable revenue lifecycle management.
• Experience managing complex data ecosystems with numerous integrations across CRM, ERP, marketing, support, and analytics platforms.
• Experience establishing data governance and lifecycle management practices, including data quality standards, master data management, and platform rationalization.

• Hands-on experience with modern data warehouse platforms (e.g., Snowflake, BigQuery, Redshift, or similar).
• Experience with data transformation and pipeline tools (e.g., Airflow, or similar).
• Familiarity with BI and analytics platforms (e.g., Tableau, Power BI, Looker, or similar).
• Experience delivering embedded analytics or customer-facing reporting within a SaaS product environment.
• Experience with CRM and ERP data integrations, particularly Salesforce and NetSuite.
• Familiarity with AI/ML platforms or data science tooling and the ability to partner effectively with AI-focused teams.
• Experience operating in regulated or compliance-driven environments.
• Experience using modern AI productivity tools (e.g., Claude, ChatGPT, Copilot, or similar platforms) to support operational analysis, data analysis, data engineering, and documentation.
• Experience with data cataloging, lineage, or observability tools (e.g., DataDog, Glue, or similar).

• Leadership and Product have reliable, consistent data and reporting to support operational and strategic decision-making.
• Data Lake, Warehouse, and Reporting are designed to be scalable, cost effective, highly performant, and modern.
• Lexipol's data lake and warehouse (AWS, Redshift) are architected and maintained to be fully AI-ready — with clean, well-structured, well-documented data that can be reliably ingested and used by AI and machine learning systems.
• AI-powered capabilities are successfully delivered to both internal stakeholders and external customers, enabling richer, more intuitive access to Lexipol's data through intelligent reporting, natural language interfaces, and automated insights.
• The data team actively uses AI tools in its own development and delivery workflows, accelerating execution and setting a standard for AI-assisted practices across the broader technology organization.
• The data team is a fully integrated and high-performing contributor to the product development lifecycle, consistently delivering roadmap commitments alongside Engineering and Solutions Management.
• A mature data governance framework is in place, with clear standards for data quality, access, stewardship, and lifecycle management across both internal and product data environments.
• Analytics and BI capabilities are modernized and widely adopted, enabling self-service insights for internal stakeholders and differentiated reporting experiences for customers.
• Data infrastructure is structured and governed to support AI/ML initiatives and the responsible adoption of AI-enabled capabilities across the organization and its products.
• Software and data platform investments are optimized, with strong governance around vendor selection, renewals, and platform lifecycle management.
• Successful completion of annual SOC 2 audits with data systems and controls meeting all compliance requirements.

• Our team is highly productive and works with urgency every day to provide the expected high-quality software solutions to our public safety professionals. Individuals who seek out additional responsibilities and have an action-oriented mindset thrive on this team.
• Dynamic, collaborative, and driven by a mission to make a difference. Our team enjoys the challenge of solving complex problems and implementing solutions that have a real impact.
• We celebrate our success, give praise, want to learn from one another, and are dedicated to each member of the team maximizing their potential.
• Our core values of Serve, Innovate, Collaborate, Empower, and Own It are not just words but the principles guiding every decision and action, fostering a culture of impact, growth, and mutual respect. Like the vital relationship between a ship and its crew, we prioritize our collective mission and the well-being of our teammates. This philosophy ensures we navigate challenges successfully and support each other in achieving personal and professional growth, reflecting our commitment to service and innovation.

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