Senior Analytics Engineer

Job Title: Senior Analytics Engineer


100% Remote


NOTE: Candidate should be physically in (Brazil, Mexico, Uruguay or Argentina)


Client: Critical River | Workiva

Team Overview


The Senior Analytics Engineer at Workiva is an instrumental part in delivering analytics and insights that will drive the organization to exceed company objectives. The role interacts with diverse business and technology customers, which may be internal or external.


This role will perform statistical modeling and analysis of structured and unstructured datasets to develop metrics, reports and visualizations of trends and patterns, while leveraging programming languages like SOL and Python to clean, manipulate and analyze complex datasets.

What You'll Do

• Gather and analyze feedback from existing customers to evaluate new enhancements, features, and solutions
• Validate product features with customers and conduct walkthroughs of product iterations during development
• Collaborate with customer teams to ensure features are user-friendly and intuitive
• Design queries to extract data to generate dashboards and reports to meet business users' operational needs and support business decisions
• Develop automation and integration of data and processes
• Identify appropriate data sources, report metrics, and tools for providing required information according to the stakeholder's specifications
• Define and/or ensure established report/analytics parameters are consistent with data governance requirements (e.g., share minimally necessary data)
• Collaborate with internal business partners to understand requirements and appropriately frame them into data and reporting solutions
• Improve data availability by acting as a liaison between the Data Warehouse team and other business teams within Workiva
• Implement and maintain data quality checks and monitoring systems to ensure data integrity
• Apply analytical, mathematical, and statistical techniques to investigating and interpreting data results
• Maintain a strong knowledge of Workiva products through a commitment to ongoing training
• Keep up to date on best practices, new technologies, and relevant industry news

What You'll Need

Minimum Qualifications

• 4+ years of relevant experience in a business intelligence role, including data warehousing and business intelligence tools, techniques, and technology, or experience in analytics, business analysis or comparable consumer analytics solutions
• Undergraduate degree or equivalent combination of education and experience. Degree in Computer Science, Engineering, Math, Finance, Statistics or related discipline
• Demonstrated experience using dbt for data transformation and modeling, including the ability to write and manage SQL models

Preferred Qualifications

• 2-3 years of previous statistics experience preferred
• Extensive knowledge and experience in data mining, SQL (including query design and tuning for performance), and using databases in a business environment with large-scale, complex datasets. Familiarity with business intelligence reporting tools (e.g., QuickSight, Tableau, Power Bl)
• Experience with Python, R, or other relevant scripting languages preferred
• Extensive knowledge of customer relationship management (CRM) tools (e.g., Salesforce, HubSpot), specifically in report design, dashboard design, and custom objects
• Familiarity with cloud-based data warehousing solutions (e.g., AWS Redshift, Databricks, Snowflake)
• Proficient research skills to locate market information using various internal and external data sources
• Excellent verbal and written communication and interpersonal skills, with the ability to effectively communicate with both business and technical teams
• Excellent planning and organizing skills to prioritize projects and ensure data is delivered accurately and understandably
• Familiarity with data structures and schemas from various systems (ERP, GRC, GL) and an understanding of API design philosophy/patterns and communication protocols (SOAP, REST, JSON, XML)
• Experience in designing and implementing data models for relational and non-relational databases, including both conceptual and physical modeling