Role Description Atuar na construção e evolução da camada analítica da organização, transformando dados brutos em produtos de dados confiáveis, documentados e reutilizáveis. Será responsável por estruturar modelos analíticos, Data Marts e métricas corporativas que suportem iniciativas de Business Intelligence, Analytics e Machine Learning, garantindo qualidade, governança, escalabilidade e alinhamento com as necessidades do negócio. Qualifications - Ensino Superior completo em: - Ciência da Computação - Engenharia da Computação - Sistemas de Informação - Ciência de Dados - Estatística - Matemática - Engenharia ou áreas correlatas - DBT avançado - SQL avançado - Modelagem dimensional e analítica de dados - Experiência com Data Warehouse e arquiteturas Lakehouse - Conhecimento sólido em camadas analíticas (Silver, Curated, Gold e Data Marts) - Boas práticas de qualidade, testes e governança de dados - Versionamento de código utilizando Git e GitHub - Documentação técnica e funcional de modelos de dados - Definição, padronização e governança de métricas corporativas - Estruturação de datasets para BI, Analytics e Machine Learning Requirements - Excelente comunicação com áreas técnicas e de negócio - Forte capacidade analítica e resolução de problemas - Organização e atenção aos detalhes - Visão estratégica aplicada a dados e produtos analíticos - Capacidade de traduzir demandas de negócio em soluções de dados - Proatividade na identificação e implementação de melhorias - Facilidade para atuação em equipes multidisciplinares - Senso de responsabilidade sobre qualidade e confiabilidade dos dados - Mentalidade orientada a produto de dados - Capacidade de atuar de forma autônoma e com protagonismo técnico Benefits - Experiência em projetos de Modern Data Stack - Vivência em ambientes Lakehouse e Data Warehouse corporativos - Certificações em Dados, Analytics, Cloud ou DBT - Experiência com plataformas cloud (AWS, Azure ou GCP) - Conhecimento em Databricks, Snowflake, BigQuery, Microsoft Fabric ou tecnologias similares - Experiência com Feature Store e preparação de dados para Machine Learning - Vivência em definição de métricas corporativas e camada semântica - Conhecimentos em Data Observability e Data Quality - Inglês técnico para leitura de documentação e interação em ambientes corporativos globais Scope of Work O profissional atuará na construção e evolução da plataforma analítica da organização, sendo responsável por transformar dados em ativos confiáveis para tomada de decisão, promovendo governança, escalabilidade e padronização dos produtos de dados consumidos por áreas de negócio, Analytics, BI e Machine Learning.

• Own end-to-end analytics initiatives from stakeholder discovery through implementation, validation, automation, and business adoption. • Partner with business, BI, data, and technical stakeholders to clarify objectives, define success metrics, and convert ambiguous requests into actionable analytics work. • Design, write, optimize, and validate advanced SQL queries against large-scale enterprise datasets. • Develop scalable analytics solutions that support operational decision-making, product insights, root-cause analysis, and recurring business reporting. • Apply AI and automation to improve BI workflows, including natural language-to-SQL, BI copilots, automated insight generation, anomaly/root-cause workflows, and data-quality monitoring. • Build, evaluate, test, and refine AI-assisted analytics workflows to ensure accuracy, explainability, repeatability, and business trust. • Identify opportunities to reduce manual reporting effort, streamline stakeholder requests, improve analytical throughput, and increase BI team productivity. • Validate analytical outputs through rigorous data-quality checks, metric reconciliation, source-of-truth review, and stakeholder confirmation. • Collaborate with BI engineers, data engineers, product analysts, and business stakeholders to align data models, metric definitions, and reporting logic. • Create clear documentation for analytics logic, SQL assumptions, data lineage, AI workflow behavior, known limitations, and stakeholder-facing outputs. • Operate independently in a fast-paced environment with limited onboarding support, while proactively identifying risks, dependencies, blockers, and decisions needed. • Communicate insights, tradeoffs, risks, and recommendations clearly to both technical and non-technical stakeholders. • Support adoption of analytics and AI-enabled BI solutions by training stakeholders, gathering feedback, measuring usage, and iterating on workflows. • Help define reusable patterns for agentic BI delivery, including evaluation methods, validation checklists, governance practices, and automation playbooks. • Mentor analysts or engineers on advanced SQL, AI-assisted analytics workflows, business framing, and end-to-end project ownership.

Description Work arrangement : Remote: This role is based remotely but if you live within a 50-mile radius of [Atlanta, Austin, Detroit, Warren, Milford or Mountain View], you are expected to report to that location three times per week, at minimum. The Safety Assurance for Effective Autonomous Driving Software (SAFE-ADS) department is part of GM's Global Product Safety, System, and Certification organization. Our mission is to help GM deliver trustworthy automated-driving products. As the central authority for automated driving system safety, SAFE-ADS brings together experts from across the company to develop and maintain a comprehensive safety case, including safety performance indicators for GM's automated-driving technologies. GM's vision is zero crashes, zero emissions, and zero congestion, and autonomous vehicle safety is essential to achieving that vision. The Team The AV Safety Engineering Analytics team supports safety-related decision-making across GM by developing analytics, metrics, and evidence from vehicle, simulation, and external data sources. The team supports both proactive safety monitoring and targeted investigations, and works across stakeholder groups to support engineering, validation, verification, and program decisions by turning complex technical data into usable guidance. The Role The AV Safety Engineering Analytics Engineer is an engineering role with a strong safety data science applied to physical systems focus, centered on developing the analyses, metrics, and evidence used to evaluate automated driving system safety and support decision-making. In this role, you will combine engineering judgment, data analysis, and statistical thinking to transform raw vehicle, simulation, and external data into safety metrics, investigations, and stakeholder-facing insights. You will work with cross-functional partners to define and productionize safety-relevant metrics, establish evidence and sufficiency criteria used to assess system performance and launch readiness, and communicate findings clearly to stakeholders. This role regularly supports systems, safety, testing, and verification activities by helping translate data into decision-useful metrics and evidence. Interactive visualizations and scalable data pipelines are important enablers in this role, helping analyses scale, increasing transparency, and turning complex results into usable stories for decision-making. What You'll Do - Define, prototype, and productionize safety and performance metrics for automated driving systems. - Establish analytic approaches and sufficiency criteria that support safety assessment, development decisions, and launch readiness. - Support proactive safety monitoring and targeted investigations tied to specific system-performance or safety questions. - Support systems, safety, testing, and verification stakeholders by comparing real-world and simulation-based results, identifying gaps, and helping improve the representativeness of evaluation methods. - Apply engineering and physics-based methods to process raw signals and derive meaningful representations of vehicle motion, driving context, and system behavior. - Distinguish sensor or pipeline errors from meaningful real-world outliers using engineering judgment and data validation methods. - Create interactive visualizations and reporting artifacts that communicate safety insights clearly, enhance transparency, and reduce barriers to interrogating source data in support of technical decision-making. - Build and maintain analytics infrastructure that supports safety assurance across development, validation, and deployment. - Develop reliable pipelines that ingest, transform, analyze, and publish data from vehicle systems, internal databases, simulation outputs, and external sources. - Optimize analytics code and workflows for scalable, automated cloud execution. Your Skills & Abilities (Required Qualifications) - Bachelor's degree in Computer Science, Mechanical Engineering, Vehicle Engineering, Physics, or a related field, or equivalent practical experience - 5+ years of experience analyzing large-scale driving, vehicle, robotics, or similar engineering data - 5+ years of experience in ADAS, autonomous vehicles, robotics, or a related technical domain - Experience with statistics relevant to large-scale engineering data analysis, including sampling, bias management, and experimental design - Experience transforming noisy time-series or sensor data into analysis-ready features or metrics - Strong problem-solving skills and a proactive, learning-oriented mindset - Strong communication and collaboration skills, with the ability to work effectively across technical teams - Strong programming skills in Python and SQL - Experience building and operating cloud-based analytics or data-processing workflows at scale - Experience in some combination of the following is expected: - Programming & Frameworks : Python, SQL - Cloud & Big Data : cloud-based large-scale processing including notifications, queuing, serverless functions, event-driven processing, infrastructure as code, containerization, process monitoring, process optimization, identity and access management, and service-to-service access - Statistics : descriptive statistics, managing bias in large data mining activities, experimental design, and sampling strategies - DevOps / Infrastructure as Code : CI/CD, versioning, Docker, Kubernetes, GitHub, Jira, Jenkins, Poetry, Terraform - Data Analysis & Visualization: Tableau, PowerBI, Plotly/Dash, Shiny, Pandas, NumPy What Will Give You a Competitive Edge (Preferred Qualifications) - Experience analyzing large-scale vehicle motion, driving context, automated-driving performance, or simulation data - Experience with driver behavior modeling, human performance benchmarking, causal inference, or counterfactual modeling techniques - Experience with systems engineering, verification and validation, simulation-based evaluation, scenario analysis, or work that bridges simulation and on-road safety assessment - Experience building stakeholder-facing dashboards or interactive analytics products - Experience with cloud or distributed data platforms, or with DevOps, CI/CD, containerization, or infrastructure-as-code workflows - Publications, conference participation, or other demonstrated engagement in vehicle-safety, safety-analytics, or related technical work GM DOES NOT PROVIDE IMMIGRATION-RELATED SPONSORSHIP FOR THIS ROLE. DO NOT APPLY FOR THIS ROLE IF YOU WILL NEED GM IMMIGRATION SPONSORSHIP (e.g., H-1B, TN, STEM OPT, etc.) NOW OR IN THE FUTURE. This job is not eligible for relocation benefits. Any relocation costs would be the responsibility of the selected candidate. #LI-SA2 About GM Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all. Why Join Us We believe we all must make a choice every day - individually and collectively - to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team. Total Rewards | Benefits Overview From day one, we're looking out for your well-being-at work and at home-so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources. Non-Discrimination and Equal Employment Opportunities (U.S.) General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers. All employment decisions are made on a non-discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws. We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire. Accommodations General Motors offers opportunities to all job seekers including individuals with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, email us [email protected] or call us at 1-800-865-7580. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.

• Own Landing's dbt models, data pipelines, and warehouse architecture. • Maintain dbt model library — keeping models clean, tested, documented, and aligned with business data usage. • Manage Landing's ELT stack — including Stitch, Snowflake, dbt, and associated monitoring and orchestration tooling. • Establish a finance and operations data spine — authoritative source of truth for core executive KPIs, metric definitions, and warehouse logic. • Develop analytics engineering standards, tooling decisions, and long-term roadmap for data platform. • Foster cross-functional data partnerships with Engineering, FP&A, and Accounting — translate between technical infrastructure and business need. • Enforce data quality standards, testing frameworks, and metric governance practices. • Support executive, board, and investor reporting — work directly impacts high-stakes decision-making and fundraising.