Engineer, Customer Support and Analytics

General Motors•Warren, MI

1d•Hybrid

About The Position

The Connected Ops Support and Analytics team protects and improves the connected-vehicle experience across vehicle and digital services. As a Technical Operations Engineer, you’ll support Customer identity workflows, digital services, and Connected services performance health. You’ll investigate customer-impacting issues, turn data into actionable insights, and partner with engineering, application, and contact-center teams to improve reliability and customer outcomes. This technical individual contributor role is an opportunity to connect system behavior to customer experience and deliver practical improvements across the connected-vehicle lifecycle.

Requirements

Bachelor’s degree in computer science, engineering, information systems, data analytics, or a related field, or equivalent professional experience.
3+ years of experience in technical operations, data analytics, systems analysis, software engineering, product support, or a related discipline.
Demonstrated experience analyzing complex systems, application interactions, customer-impacting issues, or operational workflows.
Strong SQL skills, including querying across multiple tables, data transformation, aggregation, validation, and performance-aware query design.
Experience building or supporting dashboards and operational reporting in Power BI, Tableau, or a comparable visualization platform.
Ability to break down business or technical questions, identify relevant data, follow an analysis plan, and communicate clear conclusions.
Experience performing root-cause analysis and converting analytical findings into measurable corrective actions.
Strong written and verbal communication skills, with the ability to manage multiple priorities and make sound decisions with incomplete information.

Nice To Haves

Working knowledge of Databricks, Python, Power BI, and data engineering or extract, transform, and load (ETL) concepts.
Familiarity with artificial intelligence tools and the ability to integrate them responsibly into data exploration, documentation, issue triage, reporting, and process improvement.
Experience with Datadog, Azure Analytics, cloud-based data platforms, or comparable observability and analytics tools.
Familiarity with application programming interfaces (APIs), event-driven architectures, identity management, mobile applications, and connected-vehicle or digital-service ecosystems.
Experience with large-scale data technologies such as Hadoop, Teradata, or similar platforms.
Familiarity with customer-contact-center applications, case-management workflows, XM Discover, Maxis, or related customer-experience tools.
Experience defining operational key performance indicators (KPIs), service-level measures, data-quality checks, or monitoring strategies.
Knowledge of experimentation, controlled validation, process capability, or continuous-improvement methods.
Exposure to process or application changes involving multiple technical and business organizations.
Collaborative, curious, and proactive approach, with the ability to build trust and drive progress across teams without direct authority.

Responsibilities

Analyze end-to-end customer journeys, operational processes, and system performance to identify trends, defects, risks, and improvement opportunities.
Resolve complex customer cases as well as create processes and best practices for routine escalations supporting team members will execute.
Lead root-cause investigations for production incidents, recurring defects, and performance degradation across connected services.
Build and maintain performance metrics, dashboards, and reports that show customer impact, operational health, issue trends, and progress against goals.
Define analytical requirements, data sources, measurement strategies, and validation plans for new capabilities and process changes.
Write and optimize SQL queries to join, cleanse, aggregate, and analyze data from multiple systems while assessing data quality and documenting assumptions.
Apply trend analysis, anomaly detection, correlation analysis, and hypothesis-based validation to support fact-based decisions.
Use Databricks, and related analytics technologies to automate recurring analysis, investigate large datasets, and improve the scalability of operational insights.
Translate technical findings into clear recommendations and communicate issue priority based on customer impact, business risk, frequency, severity, and available evidence.
Partner with engineering, technology, and contact-center operations to contain customer-impacting issues and implement sustainable system or process improvements.
Monitor post-release and post-remediation performance, document results, and contribute to structured triage, knowledge sharing, and consistent operational practices.