Data Vizualization Engineer

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

The Customer Data Intelligence team in Success organization structures customer data and creates data science/intelligence products that drive customer success. The team works with executives, success managers, sales and renewal managers, and product managers across CSG to discover and analyze opportunities for growth and optimization and to deliver useful insights. The team is a mix of data scientists, data engineers, data architects, data insights analysts, & data visualization engineers.

Responsibilities:

• Develop: Build data sets and dashboards in Tableau that can be used by business partners to track business critical metrics and derive useful insights to optimize future strategies.
• Design: Build and implement complex data models for the use in self service analytics tools such as dashboards, consoles and query driven bots.
• Integrate: Engineer integrations between communication and presentation applications such as Slack and Tableau.
• Innovate: Apply myriad technologies across the tech stack (Slack, Tableau, MuleSoft, Snowflake, Hadoop, Python, ETL tools, etc.) to build innovative applications to deliver analytics and insights.
• Engineer: Design and develop testing frameworks to test ETL jobs, reports, and Dashboards delivery, and other data pipelines
• Code: Craft and write sophisticated calculations, formulas and queries to ensure accurate and dynamic presentation of data delivered through tools such as Tableau and Slack.
• Analyze: Analyze large amounts of business data to develop strategic insights or tackle key business problems.
• Design: Build repeatable processes and reporting packages to empower both Customer Success leaders and their teams with critical operational data related to strategy and execution.
• Iterate: Improve and maintain standardized operational reports and dashboards to monitor overall health, data integrity, and other key objectives and performance indicators. Support ongoing analysis of business performance. Inform executive decisions.
• Project Management: Run analytics projects/programs through their lifecycle; Create strategies for risk mitigation and contingency planning; Oversee work on projects where necessary; Efficiently identify and resolve program issues; Design and maintain technical and project documentation.
• Partner: Build strong working relationships with internal teams, including data engineering, operations, product management, and business customers to support analytical product development and measuring impact and adoption.
• Communicate: Engage with internal teams to showcase results and drive adoption of standard methodologies.
• Grow: Regularly assess and improve your own performance in such areas as job knowledge, technical skills, productivity, accountability, quality, initiative, innovation, drive, working relationships, communications, leadership, and alignment with the Salesforce core values.
• Enable: Mentor junior team members, super users across the business, and members of the larger Customer Success data visualization community.

Skills and Experience

• Business analytics degree (e.g. Business Analytics, Applied Mathematics, Financial Mathematics, Economics, Cognitive Sciences, General Engineering, or Information Systems/Technology).
• Significant experience or coursework applying analytical skills in a business context.
• Working knowledge with one or more Business Intelligence or visualization tools such as Tableau, Qlik, Business Objects.
• Working knowledge of SQL: Oracle, MySQL, SQL Server, Redshift, etc.
• Working knowledge of API protocols for application integration.
• Understanding of Statistical Modeling techniques such as Regression, Clustering, Time-Series, etc.
• Understanding of enterprise application architecture, including; Integration principles and tools, Enterprise Data Management tools and methods, API design patterns, Process orchestration and Data Visualization.
• Solid attention to detail to deliver accurate conclusions and proven recommendations.