Senior Manager, Data Analytics

Job Summary

The Senior Manager, Analytics leads a high-performing team of Data Product Managers to deliver data-driven insights, AI initiatives and analytics products that accelerate business growth and support critical decision-making. This role partners closely with stakeholders across Finance, Sales, Marketing, People, Professional Services and Engineering to identify AI opportunities, prioritize initiatives, and deliver analytics solutions for core business processes. 

Roles & Responsibilities

1. Strategy and Stakeholder Partnership 

• Develop and own the Analytics and Enterprise AI strategy and roadmap for key business areas, ensuring alignment with enterprise data management and AI objectives.
• Collaborate with senior business stakeholders to scope high-impact problems, define success metrics, and co-create analytics roadmaps that inform product development and operations.
• Champion data-driven decision-making by promoting consistent KPIs, self-service analytics tools, and evidence-based recommendations at the executive level. 

2. Team Leadership and Development 

• Lead and expand a team of Data Product Managers, including hiring, coaching, performance management, and career development.
• Foster a collaborative, inclusive environment that encourages innovation, experimentation, and continuous improvement in analytics tools, methods, and processes. 

3. Analytics Delivery and Data Products 

Core Analytics & Insights 

• Oversee the design, execution, and delivery of advanced analytics, predictive models, and data products using modern cloud-based data platforms.
• Guide Data Product Managers in building reusable semantic layers, dashboards, and ML-powered insights tailored to stakeholder needs.
• Ensure analytical rigor through data validation, peer reviews, and comprehensive documentation; translate complex findings into clear, actionable recommendations for non-technical audiences. 

Data as a Product 

• Champion a "data as a product" mindset by partnering with domain owners to deliver trusted, well-documented datasets with clear ownership and defined SLAs.
• Drive adoption of an enterprise data catalog to enable self-service data discovery, document data lineage, and provide transparency into data assets across the organization.
• Own the enterprise business glossary in partnership with business stakeholders, ensuring consistent definitions and semantic alignment across reports, metrics, and data products. 

Data Quality & Profiling 

• Lead data profiling initiatives to assess source data for completeness, accuracy, consistency, and fitness for analytics and AI use cases.
• Define and enforce data quality rules, thresholds, and scorecards across critical data domains; establish remediation workflows to address issues before they impact downstream consumers.
• Define success criteria, data dependencies, and certification standards within owned functional domains. 

AI, Machine Learning & Generative AI 

• Design data ecosystems that support advanced analytics, machine learning, and AI-driven insights—ensuring structured and unstructured data (including documents and logs) are accessible, reliable, and actionable.
• Demonstrate hands-on experience with AI agents and generative AI, including building and integrating conversational bots, autonomous agents, and generative AI models into enterprise workflows.
• Evaluate generative AI frameworks, develop governance around prompt engineering and model outputs, and guide teams on safely incorporating these technologies into products. 

AI Governance & Compliance 

• Establish data governance best practices for AI, including metadata tagging for training data, model lineage tracking, bias detection, and privacy controls.
• Ensure AI data pipelines comply with ethical and regulatory requirements (e.g., GDPR, CCPA) and align with enterprise governance frameworks. 

4. Platform and Process Ownership 

• Partner with data engineering, analytics engineering, and BI teams to enhance data pipelines, governance, and analytics tooling.
• Define and govern key metrics, data quality frameworks, and compliance standards, integrated with enterprise MDM and AI workflows.
• Establish and operationalize data quality monitoring frameworks integrated with data pipelines to proactively detect anomalies, drift, and SLA breaches before impacting business decisions.
• Implement data profiling automation as part of onboarding new data sources into the analytics ecosystem, reducing time-to-insight and mitigating downstream quality risks.
• Evaluate and drive adoption of analytics tools, and experiment with modern formats such as LLMs, agentic workflows, and Apache Iceberg for efficiency gains. 

5. Cross-Functional Impact 

• Lead analytics for cross-functional initiatives, ensuring measurement plans are in place from the start and drive iterative improvements.
• Manage change related to the rollout of new data products or metrics, including leading training, adoption efforts, and gathering stakeholder feedback.
• Proactively communicate data limitations, risks, and ethical considerations to guide pragmatic stakeholder decisions.
• Collaborate with data scientists and ML engineers to design feature engineering pipelines, model training datasets, and MLOps workflows.
• Oversee the development, deployment, and monitoring of AI models, ensuring business objectives are met and measurable value is delivered. 

Qualifications & Educational Requirements

• 8+ years of experience in data analytics, with at least 3 years of managing data product managers teams in enterprise environments
• Proven ability to influence and collaborate with senior stakeholders.
• Successful experience hiring, coaching, and developing high-performing analytics teams.
• Commitment to fostering an inclusive, innovative, and performance-driven team culture.
• Experience with data catalog tools (e.g., Alation, Collibra, Atlan) and metadata management
• Experience with data quality tools/frameworks (e.g., Great Expectations, Monte Carlo, dbt tests)
• Experience with prompt engineering, AI agents, or GenAI frameworks
• Excellent communication skills: able to translate complex data concepts for technical and non-technical audiences and influence senior leaders
• Demonstrated cross-functional collaboration, especially with engineering, operations, product management, and business units.
• Strong strategic thinking and problem-solving skills; ability to translate business strategy into scalable data architecture. 

Sponsorship

• This position is eligible for visa sponsorship. Note: Tekion does not sponsor H-1B Cap Case petitions. 

Perks and Benefits

• Competitive compensation and generous stock options   
• 100% employer-paid top-of-the-line medical, dental and vision coverage  
• Great benefits including unlimited PTO, parental leave and free snacks and beverages  
• The opportunity to work with some of the brightest minds from Silicon Valley’s most dominant and successful companies   
• Be part of an early stage, hyper-growth start-up with the opportunity to grow and prosper    
• Work on the latest and coolest technologies – everything is home-grown and built ground-up   
• A dynamic work environment with a strong sense of community and collaboration   
• The open and transparent culture that encourages innovation, rewards performance and discourages hierarchy   
• Exciting opportunities for career growth and development   

Current Tekion Employees – Please apply via Greenhouse Internal Job Board