Senior Manager of AI Data Analytics, Tekion
$191-286.5k
Plus stock options
More information about location
Office located in Pleasanton, CA
End-to-end cloud native automotive platform
Open for applications
End-to-end cloud native automotive platform
1001+ employees
Open for applications
$191-286.5k
Plus stock options
More information about location
Office located in Pleasanton, CA
1001+ employees
Company mission
To build the world's best business applications in the cloud.
Role
Who you are
- 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
What the job involves
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
Our take
Car dealerships suffer from an inefficiency problem. The paperwork and administration required to complete a sale requires dealers to manage a disjointed set of various software applications. Tekion is offering an enticing remedy - a cloud-based automotive retail system that integrates the various software applications in a single infrastructure.
Competition has been strong, with rivals CDK Global and Reynolds & Reynolds offering a competitive raft of features, and benefiting from strong prior relationships with dealerships. Tekion, however, has nevertheless secured customers for the software in 28 states - likely in part due to the efficiency of partnering with a single growing service as opposed to a disparate spread.
This momentum shows no signs of slowing either, as Tekion has attracted attention from major players like CMC, Chrysler, and Chevrolet. By streamlining dealership operations into a single platform, the company is proving that even in a crowded market, innovation and simplicity can win over both dealers and industry partners.
Freddie
Company Specialist at Welcome to the Jungle
Insights
25% employee growth in 12 months
Company
Funding (last 2 of 6 rounds)
Jul 2024
$200m
GROWTH EQUITY VC
Oct 2021
$250m
SERIES D
Total funding: $635.1m
Company benefits
- Silicon Valley Startup Environment
- Stock Options
- Focus on Work/Life Balance
- Recognition Culture
- Health and Wellness
- Unparalleled Career Development
Company HQ
Pleasanton, CA
Leadership
Jay Vijayan
(Co-Founder & CEO)They started their career at Oracle, where they spent over 7 years in Management before spending 4 at VMware. They then worked at Tesla for 4 years, leaving their position as CIO to found Tekion in February 2016.
Guru Sankararaman
(Co-Founder & COO)After working as a Chief Architect at Cisco, they joined Tesla for 4 years. Initially they served as Director of Information Technology and then later became VP of Information Technology. Left this role to co-found Tekion in 2016.
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