Contact Center Data Engineer II

GEICO•Tampa, FL

1d•$88,150 - $157,850•Hybrid

About The Position

The Contact Center Data Engineer II is an experienced analytics professional responsible for building reliable data solutions, producing actionable insights, and helping guide more complex analytics work across contact center operations, IVR, automation, workforce management, product, and technology functions. This role carries the same core responsibilities as the Data Engineer I role, but with greater ownership, stronger technical depth, and a higher expectation to lead analysis, influence decisions, improve data practices, and help less-experienced team members solve problems effectively. This is a hybrid position, requiring on-site presence 2-3 days a week at one of the following locations: Dallas, TX; Palo Alto, CA; Bethesda, MD; Buffalo, NY; San Diego, CA; Virginia Beach, VA; Fredericksburg, VA; Tampa, FL.

Requirements

2+ years of experience in business analytics, data analytics, product analytics, operations analytics, or a related analytical role.
2+ years of experience querying complex systems using SQL and performing data analysis.
2+ years of experience using analytical or programming languages such as Python, R, SAS, .NET, M, or DAX.
1+ years of experience using source control, DevOps tools, or collaborative development practices.
2+ years of experience developing business intelligence reporting or data visualizations in Power BI, Amazon QuickSight, Tableau, or similar tools.
Experience analyzing operational data, identifying root causes, and translating findings into business recommendations.
Ability to manage more complex assignments, work across multiple business partners, and operate with limited direction in a fast-moving environment.
BA/BS degree in computer science, statistics, analytics, operations research, economics, mathematics, or a related field.

Nice To Haves

Experience supporting contact center, IVR, customer service, workforce management, digital servicing, insurance, or financial technology environments.
Experience using Amazon CloudWatch or similar tools to monitor system, service, or operational performance and identify anomalies.
Experience with big data, cloud-based technologies, data warehousing, or data engineering concepts.
Experience working in Agile delivery environments with product owners, engineers, and business stakeholders.
Experience mentoring peers, reviewing analytics work, improving reporting standards, or helping teams adopt consistent development practices.

Responsibilities

Design, develop, test, deploy, and maintain more complex data structures, enterprise reports, dashboards, analytical systems, and visualizations that support operational and strategic decision-making.
Lead analysis of contact center, IVR, digital, customer, workforce, and system performance data to identify root causes, quantify business impact, and recommend practical actions.
Build and enhance reporting solutions using Power BI, SQL, Amazon QuickSight, Amazon CloudWatch, and related analytics or monitoring tools used by the team.
Proactively detect operational or system issues by monitoring performance patterns, outliers, anomalies, and metric movement across contact center and IVR environments.
Translate complex business needs into technical requirements and scalable reporting or analytical solutions that can be used by operations, product, automation, workforce management, and technology partners.
Evaluate new internal and external data sources, validate data quality, and recommend ways to expand data warehouse, reporting, and self-service analytics capabilities.
Assist with the assessment and cultivation of long-term business intelligence, automation, and operational insight opportunities in partnership with business, engineering, data architecture, and data science teams.
Improve reporting standards, metric definitions, documentation, testing practices, and data governance controls for assigned subject areas.
Support and coach less-experienced analysts or engineers by reviewing work, sharing best practices, and helping the team use consistent development and validation practices.
Communicate complex insights clearly to technical and non-technical stakeholders and influence decisions through strong data storytelling, business context, and practical recommendations.