Senior Manager, Data Analytics

Tekion

About The Position

Positively disrupting an industry that has not seen any innovation in over 50 years, Tekion has challenged the paradigm with the first and fastest cloud-native automotive platform that includes the revolutionary Automotive Retail Cloud (ARC) for retailers, Automotive Enterprise Cloud (AEC) for manufacturers and other large automotive enterprises and Automotive Partner Cloud (APC) for technology and industry partners. Tekion connects the entire spectrum of the automotive retail ecosystem through one seamless platform. The transformative platform uses cutting-edge technology, big data, machine learning, and AI to seamlessly bring together OEMs, retailers/dealers and consumers. With its highly configurable integration and greater customer engagement capabilities, Tekion is enabling the best automotive retail experiences ever. Tekion employs close to 3,000 people across North America, Asia and Europe. 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.

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.

Responsibilities

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.
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.
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.
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.
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.

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

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