Senior Contact Center Data Engineer

GEICO•Bethesda, MD

5d•$104,550 - $172,200•Hybrid

About The Position

The Senior Contact Center Data Engineer I is a senior individual contributor responsible for building scalable analytics solutions, guiding complex analysis, and helping mature the contact center analytics capability. This role supports contact center operations, interactive voice response (IVR), automation, workforce management, product, technology, and data governance partners. This position carries greater ownership for complex data solutions, deeper technical troubleshooting, stronger proactive issue detection, and a higher expectation to mentor junior engineers and influence analytics standards across the team.

Requirements

3+ years of experience in business analytics, data analytics, product analytics, operations analytics, or a related analytical role.
3+ years of experience querying complex systems using SQL and performing data analysis.
3+ years of experience using analytical or programming languages such as Python, R, SAS, .NET, M, or DAX.
2+ years of experience using source control, DevOps tools, or collaborative development practices.
1+ years of experience with big data or cloud-based technologies such as Snowflake, Hive, Spark, Hadoop, Azure, AWS, or similar platforms.
3+ years of experience with a BA/BS degree or 5+ years of equivalent experience.
Ability to operate independently, manage complex priorities, review technical work, and communicate clearly across business and technology audiences.
BA/BS degree in computer science, statistics, analytics, operations research, economics, mathematics, business, or a related field.

Nice To Haves

3+ years of experience developing business intelligence reporting or data visualizations in Power BI, Amazon QuickSight, Tableau, or similar tools.
1+ years of experience with data engineering or systems design concepts, such as entity relationship diagrams, unified modeling language, topology diagrams, or similar design practices.
Experience supporting contact center, IVR, customer service, workforce management, automation, digital servicing, insurance, or financial technology environments.
Experience using Amazon CloudWatch or similar monitoring tools to identify operational, system, or service anomalies.
Experience working in Agile delivery environments and mentoring or coaching less-experienced analysts or engineers.

Responsibilities

Independently design, develop, test, deploy, and maintain complex data structures, enterprise reports, dashboards, analytical systems, and software that support operational and strategic decision-making.
Lead analysis of contact center, IVR, digital, cloud, customer, workforce, and operational data to identify root causes, quantify business impact, and recommend practical actions.
Build, enhance, and troubleshoot reporting and monitoring solutions using Power BI, SQL, Amazon QuickSight, Amazon CloudWatch, and other approved analytics, cloud, and data visualization tools.
Proactively identify emerging business or system issues by monitoring data patterns, anomalies, customer contact behavior, IVR performance, containment, transfers, service levels, handle time, and other operational indicators.
Partner with operations, workforce management, IVR, automation, product, engineering, data architecture, data science, and governance teams to translate complex business problems into scalable technical solutions.
Research and query new internal and external, structured and unstructured data sources to expand reporting capabilities, improve data availability, and strengthen business intelligence solutions.
Assess and cultivate longer-term opportunities for business intelligence, automation, self-service improvement, and proactive performance management.
Improve development practices, reporting standards, metric definitions, documentation, validation routines, governance controls, and data stewardship practices for assigned subject areas.
Protect sensitive data, support appropriate data classification and access controls, and ensure analytics assets align with governance and quality expectations.
Instruct, review, and coach junior Data Analytics Engineers by providing technical guidance, reviewing work, reinforcing best practices, and helping the team solve complex problems more consistently.
Communicate complex insights clearly to technical and non-technical stakeholders, using strong business context and data storytelling to influence decisions.