Databricks is hiring an experienced analytics leader to grow the function globally, as the company continues its hyper-growth and leadership in the Data and AI space. You will be a member of the CS leadership team, using all our resources to help determine important metrics driving the post sales strategy. You will lead the Customer Success data architecture and strategy, including defining our core Indicator's. You will lay the foundation of the data and analytics powerhouse of the CS team and scale your team over time. As you build our data infrastructure, you will help the CS Operations team to analyze the effectiveness of our post sales programs and the health of our customers, and eventually use AI & ML capacities to inform decisions.
On a daily basis, you will build data pipelines around product telemetry, business systems data, and aggregated tables and work with the centralized IT team to attach the CS teams data to data or other go to market teams' data. You will also own developing a BI layer on top of the CS data to provide both Leadership and individual contributors with the right information to manage their business or customer conversations. This data architecture will become the foundation to help you to expand your responsibilities into analytics and predictions. As part of the broader CS Organization, you will continue to strengthen your technical expertise through mentorship, learning, and internal training programs. You will report to VP of Customer Success, Strategy and Operations.
The impact you will have:
- Help design and operationalize the metrics that guide the CS organization including managing the data layer to provide visibility into this metrics, and self-service tooling for Customer Success teams
- Productionalize and unify existing data pipelines that are used across CS teams.
- Become an expert in product telemetry and business systems data, work with CS teams to understand customer use cases to identify behaviour patterns that guide usage of Databricks products
- Work with Executive staff to operationalize insights and help customer-facing teams to be more productive by using your insights.
- Ensure data quality across several systems of record and flag areas of continuous improvement for our data lake
- Address deep-dive questions by the Customer Success leadership team with follow up insights and recommended actions
What we look for:
- Technical background or degree, with experience in Marketing Analytics, Sales Analytics, or CS Analytics.
- Experience growing and scaling a top-notch analytics team to meet the needs of partners.
- Have built, developed, maintained and evolved complex analytical models used to guide company goals.
- Experience working with global CS leadership providing analysis and nuanced insights for the regions and departments.
- Experience integrating, cleansing data sources and using statistical modeling to solve business problems.
- Build and manage our data infrastructure to create the right data with minimal lift using Databricks, Tableau and Fivetran. Comfortable programming in SQL, Python, Scala or Java
- Experience designing solutions on cloud infrastructure and services, such as AWS, Azure, or GCP
- Comfortable presenting to strategic partners/leadership, and partnering with internal partners to design the data architecture required to report on all our processes.
- Comprehensive health coverage including medical, dental, and vision
- 401(k) Plan
- Equity awards
- Flexible time off
- Paid parental leave
- Family Planning
- Gym reimbursement
- Annual personal development fund
- Employee Assistance Program (EAP)
Databricks is the data and AI company. More than 5,000 organizations worldwide — including Comcast, Condé Nast, H&M, and over 40% of the Fortune 500— rely on Databricks' Lakehouse platform to unify their data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe. Founded by the original creators of Apache Spark™, Delta Lake and MLflow, Databricks is on a mission to help data teams solve the world's toughest problems.
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