Sr Manager, Data & Analytics

Lennar•Sacramento, CA

1d•$166,611 - $222,148•Onsite

About The Position

The Senior Manager, Data & Analytics leads one or more teams of analysts, engineers, data scientists, and developers to deliver high-impact data insights that drive strategic business decisions. This is a hands-on technical leadership role: the Senior Manager stays close enough to the architecture and implementation to guide technical decisions, review approaches, and jump in when needed, while leading the team and translating business priorities into execution. This role involves overseeing complex operations, ensuring projects are completed on time and strategically aligned with organizational goals. The Senior Manager is expected to contribute to defining and driving the long-term strategic vision for the analytics function, and to influence senior leadership on data-driven decision-making. They collaborate with senior executives and cross-functional teams to translate overarching business strategies into large-scale analytics initiatives. The Senior Manager guides and mentors more junior managers, team leads, and data scientists, fostering a culture of innovation and continuous improvement while establishing and scaling best practices in data management and analysis. They are accountable for setting and tracking performance metrics, driving organization-wide data initiatives, and delivering high-impact results in a dynamic, fast-paced environment. Additionally, they play a key role in shaping data governance policies and ensuring that analytics efforts are scalable and aligned with long-term business objectives. Associates in this role typically have a high degree of autonomy in managing their teams and projects. They are responsible for making decisions on resource allocation, project prioritization, and team development without needing direct supervision. While they collaborate closely with senior leadership for strategic direction and stakeholder alignment, they have the authority to shape the day-to-day operations and long-term plans of their team. This autonomy allows them to respond quickly to changing business needs and drive continuous improvement within the data and analytics function.

Requirements

Bachelor's Degree in a relevant field such as Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related discipline.
A strong foundation in quantitative analysis, data manipulation, and business applications of data is essential.
5+ years of relevant experience in data analytics, data science, or a related field.
2+ years of experience in a leadership or managerial role, overseeing data teams and projects.
Ability to lead, mentor, and develop one or more diverse teams of data professionals.
Proficiency with data analytics tools, programming languages (e.g., Python, SQL), and data management platforms.
Proven experience in managing complex data projects, including resource allocation and timeline management.
Ability to translate business needs into actionable data strategies.
Expertise in implementing and enforcing data governance and compliance policies.
Strong ability to convey complex data insights to non-technical stakeholders.
Strong analytical thinking and problem-solving skills to derive actionable insights.
Ability to stay current with industry trends and adapt to changing business needs.
High attention to detail to ensure accuracy and quality in data outputs.
Advanced Python Development: Writing clean, object-oriented, and production-grade Python code for complex data manipulation, automation, and API communication.
AWS Platform & Containerization: Hands-on experience deploying, managing, and scaling containerized data workloads using AWS ECS (Elastic Container Service) and ECR.
Core AWS architecture: S3, IAM, Lambda, EC2, CloudWatch, and CloudTrail.
Snowflake Data Cloud: Account administration, optimal virtual warehouse clustering strategies, and budget-optimization.
Expert feature implementation: Data Sharing, Time Travel, and Zero-copy cloning.
dbt (Data Build Tool): Managing multi-repository dbt projects and configuring dbt Cloud environments.
Creating, documenting, and optimizing advanced dbt models and custom macros.
AI-Assisted Engineering & Data Tools: Daily proficiency with AI coding assistants (GitHub Copilot, Cursor, or Claude/OpenAI APIs) to maximize development efficiency.
Familiarity with cloud-native AI services (e.g., Snowflake Cortex or AWS Bedrock) for embedding LLM capabilities directly inside the data layer.
Exposure to frameworks used for AI data preparation (e.g., LangChain, Vector Databases, or text embedding generation).
Data Ingestion & Integration: Incremental loading and Change Data Capture (CDC) methods.
Extensive experience querying and integrating with complex external REST APIs.
Version Control & CI/CD: Advanced Git/GitHub branching strategies, pull request enforcement, and automated deployment pipelines.

Nice To Haves

Certifications in data analytics, data management, or project management (e.g., Certified Analytics Professional (CAP), Project Management Professional (PMP), or relevant technical certifications) can be advantageous.
AWS Certification is a strong plus.

Responsibilities

Lead, mentor, and develop one or more teams of data analysts, engineers, and scientists, ensuring alignment with organizational goals.
Foster a collaborative and innovative team culture, promoting continuous learning and professional growth.
Oversee the planning, execution, and delivery of data and analytics projects, ensuring they are completed on time, within scope, and meet business requirements.
Prioritize and allocate resources effectively to meet project demands, balancing short-term needs with long-term strategic objectives.
Collaborate with cross-functional teams to identify and translate business needs into actionable analytics projects.
Act as the primary point of contact for stakeholders, ensuring clear communication and alignment on project goals and outcomes.
Ensure adherence to data governance policies, standards, and regulatory requirements, maintaining the integrity and security of data.
Implement and enforce best practices for data management, quality, and privacy within the team.
Track and report on key performance indicators (KPIs) for the team's projects and initiatives, ensuring alignment with business objectives.
Provide regular updates to senior leadership on the progress and impact of analytics efforts.
Contribute to the strategic direction of the data and analytics function by identifying new opportunities for leveraging data to drive business value.
Stay updated on industry trends and emerging technologies, applying innovative approaches to enhance the team's capabilities.
Continuously evaluate and improve processes, tools, and methodologies to increase the team's efficiency and effectiveness.
Ensure that the team's operations are streamlined and aligned with overall business priorities.