What are the responsibilities and job description for the Senior Manager, Data Analytics position at Mission Critical Group?
Description
The Senior Manager, Data Analytics acts as a crucial individual contributor, driving all data-informed decision-making across the company, excelling at both strategy and execution. This role is responsible for owning the entire analytics function, from data ingestion and transformation to advanced modeling, reporting, and presenting actionable insights to executive leadership. The Senior Manager, Data Analytics is technically hands-on, possesses deep business acumen, and thrives in an autonomous environment.
Requirements
Strategy & Data Governance
The Senior Manager, Data Analytics acts as a crucial individual contributor, driving all data-informed decision-making across the company, excelling at both strategy and execution. This role is responsible for owning the entire analytics function, from data ingestion and transformation to advanced modeling, reporting, and presenting actionable insights to executive leadership. The Senior Manager, Data Analytics is technically hands-on, possesses deep business acumen, and thrives in an autonomous environment.
Requirements
Strategy & Data Governance
- Develop and champion the company-wide data analytics strategy and roadmap, ensuring alignment with organizational goals.
- Establish and enforce standards for data governance, quality, and integrity across all data sources.
- Serve as the primary consultant to executive stakeholders across all departments (Marketing, Product, Finance, Operations) to identify key performance indicators (KPIs) and analytical needs.
- Design, build, and maintain data models and transformations using SQL and ETL/ELT tools to ensure data readiness for analysis.
- Conduct deep-dive, ad-hoc analyses to uncover root causes, explain performance fluctuations, and identify opportunities for optimization and growth.
- Lead the design and analysis of A/B tests and experiments, providing clear, statistically sound recommendations.
- Build and maintain all necessary dashboards and reports using visualization tools (e.g., Tableau, Power BI, Looker) to monitor business health.
- Manage and optimize the performance of the data warehouse/database in collaboration with Engineering teams.
- Evaluate, recommend, and implement new analytics tools and technologies to improve efficiency and capability.
- Automate reporting processes and analytical workflows wherever possible to maximize efficiency.
- Integrate machine learning and AI capabilities into analytics workflows to develop predictive and prescriptive models that drive measurable business outcomes.
- Apply generative AI and large language model (LLM) tools to accelerate data exploration, automate narrative reporting, and deliver executive-ready insights at greater speed and scale.
- Establish responsible AI standards and guardrails for analytics use cases, ensuring model outputs are validated, explainable, and suitable for executive decision-making and audit review.
- Safety Focus: Prioritize safety in all actions. Actively follow safety protocols, identify potential hazards, and take immediate steps to correct or report unsafe conditions. Model safe behavior for others and contribute to a culture of workplace safety.
- Have Humanity: Respect the input and ideas of everyone to create the best possible outcome.
- Be Transparent: Value transparency in all interactions, ensuring honesty and openness with each stakeholder.
- Drive Innovation: Seek new ways to improve and take on new challenges.
- Be Resilient: Adapt to the ever-evolving business needs enabled by resourcefulness and agility.
- Always Reliable: Deliver on your commitments and achieve your goals. Show up to work on time and complete your assigned tasks.
- Grit: Learn new things, actively seek feedback and training.
- Ownership Mindset: Take full responsibility for the quality and efficiency of your work. Proactively identify issues, offer solutions, and follow through. Treat company resources and goals as your own.
- Bachelor’s or Master’s degree in a quantitative field such as Computer Science, Statistics, Mathematics, Economics, or a related discipline.
- 7 years of hands-on, progressive experience in data analysis, business intelligence, or a related field.
- Expert proficiency in SQL for complex data manipulation and transformation.
- Advanced proficiency in data visualization tools (e.g., Tableau, Power BI, Looker) with a strong portfolio of impactful dashboards.
- Deep understanding of statistical methods, experimental design, and metrics construction.
- Exceptional ability to communicate complex quantitative findings to non-technical, executive audiences.
- Experience working in a high-autonomy, individual contributor analytics role.
- Demonstrated experience with predictive modeling or other advanced analytical techniques.
- Ability to take full ownership of the analytics function with minimal supervision.
- Strong ability to connect data insights directly to business outcomes and strategy.
- A "full-stack" analyst capable of handling all aspects of the data lifecycle.
- Hands-on experience building and deploying machine learning models
- Demonstrated proficiency using generative AI and LLM-based tools (e.g., Claude, ChatGPT, GitHub Copilot) to accelerate code generation, streamline data exploration, and enhance the quality and speed of analytical output.
- Familiarity with AI model explainability techniques (e.g., SHAP, LIME) and responsible AI principles, including bias detection and auditability standards applicable to business-facing analytics outputs.
- Proven ability to evaluate and operationalize emerging AI tools and capabilities within an analytics function, balancing innovation with governance and cross-functional stakeholder alignment.