Senior Analytics Engineer

Overview

The Senior Analytics Engineer plays a vital role in building data integration and warehousing solutions that support the organization's success. They prepare enterprise data for analysis to inform business strategies, combining the skills of data analysts and engineers with a focus on creating data models and communicating insights.

Sr Analytics Engineer collaborate with data teams to develop datasets and tools that empower end-users to interpret and assess data. They have diverse responsibilities including designing data models, gathering information, writing code, maintaining data documentation, communicating results, and maintaining data warehouse and analytics environments.

Responsibilities

• Collaborate with cross-functional teams to plan, design, develop, implement, manage, and maintain data integration and warehousing solutions that support business goals. This includes translating business requirements into technical specifications, developing and maintaining solution documentation and training materials, and providing ongoing user support.
• Ensure the ongoing operation of enterprise data systems related to data transfer, including designing, developing, testing, and maintaining data systems and components. Monitor and troubleshoot these systems to ensure service continuity and participate in capacity planning and architecture design.
• Serve as a subject matter expert for data principles and stay current with new data warehouse and BI technologies. Contribute to developing and documenting technical standards and best practices for the organization.
• Collaborate with cross-functional teams to design, build, test, deploy, and support solutions that meet business needs.
• Help drive improvements to the features, reliability, performance, and efficiency of Enterprise Data and Analytics platforms.
• Assist technical leadership in designing and implementing the company-wide data architecture strategy.
• Collaborate with engineering teams to identify and resolve pain points and promote best practices.
• Partner with various teams to transform concepts into requirements and requirements into services and tools.
• Engineer efficient, adaptable, and scalable architecture for all stages of the data lifecycle (ingest, streaming, structured and unstructured storage, search, aggregation) to support a variety of data applications.
• Create and maintain systems that load and transform large data sets from various internal sources.
• Serve as a subject matter expert for data-related technologies.
• Design and maintain data models for new and existing production systems and maintain data schema versioning.
• Develop and support backend components for data-centric integrations and repositories for analytical reporting.
• Work with Data Analysts and Business Intelligence team members to create data-driven insights and reports for senior management and …