Sr. Manager, Analytics Engineering

Nutanix•San Jose, CA

23h•$184,000 - $307,000•Hybrid

About The Position

Are you a visionary leader with a passion for transforming data into actionable insights, and a track record of building and inspiring high-performing analytics teams? If so, joining our team as a Senior Manager, Analytics Engineering at Nutanix will allow you to make a meaningful impact by influencing data-driven decision-making across the organization and driving the development of innovative analytics solutions in a collaborative and dynamic environment. About the Team At Nutanix, the Data Analytics team is a dynamic group of over 50 collaborative and innovative professionals spread across the US and India. This diverse team consists of data scientists, analysts, and engineers who work together to drive insights and enhance decision-making through data-driven strategies. The culture within the team fosters open communication, encourages fresh ideas, and values each member's contribution, making it an exciting environment for those passionate about analytics and technology. You will report to the Sr. Director of Data Analytics, who prioritizes mentorship and professional development while leading a supportive team environment. This position is a hybrid role, requiring you to work in the office for three days a week at our headquarters in San Jose, CA, providing the opportunity to collaborate in person with your colleagues while also allowing for flexibility. Travel requirements for this role are minimal, ensuring you can focus on your work without frequent disruptions.

Requirements

10+ years of experience in Analytics Engineering, Data Engineering, Business Intelligence, Data Analytics, or Enterprise Analytics.
5+ years leading analytics engineering teams, enterprise analytics initiatives, or cross-functional analytics delivery.
Demonstrated experience building, coaching, and inspiring high-performing technical teams.
Expert-level SQL with strong hands-on experience in dimensional modeling, semantic layers, certified datasets, analytical transformations, and data products.
Proven ability to understand complex business processes and translate them into trusted KPIs, semantic models, and governed analytical products.
Strong business acumen and experience defining enterprise KPIs with business stakeholders.
Experience with platforms such as Snowflake, Databricks, BigQuery, Redshift, or Postgres.
Experience with dbt, Spark, Python, Airflow, or similar analytics engineering technologies.
Strong understanding of data quality, reconciliation, metadata, lineage, governance, and certification.
Hands-on experience implementing AI or GenAI-enabled analytics solutions.
Excellent communication and influencing skills, with the ability to build trust and align business and technology teams.

Nice To Haves

Experience in SaaS, enterprise software, technology, or other data-intensive environments.
Experience supporting domains such as GTM, Finance, Customer Success, Marketing, Product, Operations, or Supply Chain.
Experience with Salesforce, NetSuite, Workday, SAP, Coupa, Clari, Zuora, or similar platforms.
Experience operating within a hub-and-spoke analytics model.

Responsibilities

Set the vision and direction for enterprise analytics engineering and inspire teams around trusted, scalable, and intelligent analytics.
Build, lead, and develop a high-performing team of analytics engineers and data professionals.
Create a culture of ownership, collaboration, curiosity, technical excellence, and continuous learning.
Serve as a trusted advisor to business and technology leaders by deeply understanding business processes, priorities, KPIs, and decision needs.
Translate complex requirements into semantic models, certified datasets, reusable metrics, and governed analytical data products.
Lead by example through hands-on SQL development, dimensional modeling, data validation, reconciliation, technical design, and solution reviews.
Design and implement trusted semantic layers that standardize business definitions, KPI logic, dimensions, measures, hierarchies, and reusable business rules.
Build scalable analytical models and transformations using SQL, dbt, Python, Spark, or similar technologies.
Establish strong engineering practices for architecture reviews, testing, documentation, certification, deployment, and lifecycle management.
Implement data quality, metadata, lineage, ownership, reconciliation, and governance controls.
Partner across Data Engineering, Governance, AI, BI, and distributed analytics teams in a hub-and-spoke operating model.
Implement practical AI and GenAI capabilities, including natural-language querying, semantic search, automated insights, SQL assistance, and conversational analytics.
Ensure AI-enabled analytics is grounded in trusted datasets, governed semantic models, approved KPIs, and secure access controls.
Coach and mentor team members while raising the technical and process maturity of the broader analytics community.