Director Data Services
Lexipol LLC, Frisco, TX, United States
Remote, US-based position. Candidates must already live in the United States. Must be authorized to work in the US. No visa sponsorship.
The Work The Director of Data Services sits within the Internal Technology organization and reports to the VP of Internal Technology. This role carries a unique dual mandate: delivering data infrastructure, governance, and analytics capabilities that serve both internal business operations and Lexipol's customer‑facing products. On the internal side, the Director ensures the data platforms and pipelines that power revenue, finance, marketing, and operations are scalable, well‑governed, and delivering measurable business value. On the product side, the Director and their team are active participants in the software development lifecycle — partnering with Solutions Management and Engineering to deliver data‑driven features, reporting capabilities, and customer‑facing analytics as part of the product roadmap. This leader manages a team of approximately 6–10 data professionals who operate in an agile, sprint‑based model — balancing ongoing internal data services with product roadmap commitments. The role ensures Lexipol is maximizing the value of its data assets by driving strong data quality, platform optimization, and thoughtful lifecycle management across all data systems and services. The Director partners closely with executive leadership, Solutions Management (Product Management), Engineering, Revenue, Finance, IT, and external vendors to ensure Lexipol's data platforms support operational excellence, product innovation, and strong governance.
Role Responsibilities
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.
Requirements
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.
Qualifications
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.
Preferred Experience
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).
Target Outcomes/Target Results
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.
Compensation and Benefits Lexipol offers a competitive base salary, monthly, quarterly, or annual incentive and a comprehensive benefits package including 401(k) with Company match and a flexible paid time off plan. The base salary compensation is $160,000 plus an annual performance‑based bonus.
Lexipol Is an Equal Opportunity Employer (EOE) Lexipol, LLC provides equal employment opportunities (EEO) to all team members and applicants for employment without regard to race, color, religion, gender, national origin, age, sex, pregnancy, disability, sexual orientation, gender identity or expression, veteran status, genetic information, or any other non‑job‑related characteristic. Lexipol complies with applicable federal, state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including hiring, placement, promotion, termination layoff, recall, transfers, leave of absence, compensation, and training.
#J-18808-Ljbffr
The Work The Director of Data Services sits within the Internal Technology organization and reports to the VP of Internal Technology. This role carries a unique dual mandate: delivering data infrastructure, governance, and analytics capabilities that serve both internal business operations and Lexipol's customer‑facing products. On the internal side, the Director ensures the data platforms and pipelines that power revenue, finance, marketing, and operations are scalable, well‑governed, and delivering measurable business value. On the product side, the Director and their team are active participants in the software development lifecycle — partnering with Solutions Management and Engineering to deliver data‑driven features, reporting capabilities, and customer‑facing analytics as part of the product roadmap. This leader manages a team of approximately 6–10 data professionals who operate in an agile, sprint‑based model — balancing ongoing internal data services with product roadmap commitments. The role ensures Lexipol is maximizing the value of its data assets by driving strong data quality, platform optimization, and thoughtful lifecycle management across all data systems and services. The Director partners closely with executive leadership, Solutions Management (Product Management), Engineering, Revenue, Finance, IT, and external vendors to ensure Lexipol's data platforms support operational excellence, product innovation, and strong governance.
Role Responsibilities
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.
Requirements
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.
Qualifications
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.
Preferred Experience
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).
Target Outcomes/Target Results
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.
Compensation and Benefits Lexipol offers a competitive base salary, monthly, quarterly, or annual incentive and a comprehensive benefits package including 401(k) with Company match and a flexible paid time off plan. The base salary compensation is $160,000 plus an annual performance‑based bonus.
Lexipol Is an Equal Opportunity Employer (EOE) Lexipol, LLC provides equal employment opportunities (EEO) to all team members and applicants for employment without regard to race, color, religion, gender, national origin, age, sex, pregnancy, disability, sexual orientation, gender identity or expression, veteran status, genetic information, or any other non‑job‑related characteristic. Lexipol complies with applicable federal, state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including hiring, placement, promotion, termination layoff, recall, transfers, leave of absence, compensation, and training.
#J-18808-Ljbffr