Role Description You'll be the first person at LawnStarter dedicated to data governance - the owner of whether our data can be trusted. That means the quality and freshness of our source data, pipelines, and reports; the definitions behind our metrics; the standards behind our Segment event tracking; the health of our Lightdash workspace; the data feeding our machine learning models; and the security of the data itself. This is a hands-on role. You'll work solo at first, with the Analytics team around you but nobody under you - building automation, writing checks, fixing what's broken, and putting processes in place that scale past you. If the scope grows the way we expect, this becomes the foundation of a team you'd build. What makes this role different: - You're first. Governance has been everyone's side job, so what exists today is yours to reshape - keep what works, redesign what doesn't, and your standards become the company's standards. - Whole-stack ownership. Source data to pipelines to dashboards and ML models - you own trust across the entire chain, not one slice of it. - A live migration to shape. Lightdash is landing now. You get to set up its permissions, structure, and norms before bad habits form, instead of untangling them later. What You'll Own: - Data quality and freshness - automated monitoring across source data, pipelines, and reports; catching upstream schema and source changes before they break anything downstream; running incidents to resolution when they happen. - Data lineage and impact analysis - a living map from production source to warehouse model to dashboard, and the process that uses it: when a production change is proposed, its downstream impact on pipelines, metrics, and reports gets assessed before it ships, not discovered after. - Lightdash - administration, workspace structure, permissions, and the rollout itself. Your job is to give the company self-serve autonomy while keeping the workspace tidy enough that people can find and trust what's there. - The semantic layer - we just shipped it for our most critical metrics: one governed definition per metric, in code. You'll extend definition and mapping to the rest and guard the layer against uncontrolled growth as it scales. - Event tracking governance - our governed Segment event catalog: reviewing new events against its standards, keeping it matched to what production actually sends, and evolving the guardrails (naming, property dictionary, drift detection) as tracking grows. - AI data readiness - AI agents query our warehouse every day through Brain, our internal AI toolkit. You'll govern what data AI tools can access and keep the warehouse AI-legible: documented, consistent, and safe for an agent to query and get the right answer. - Data security and privacy - access controls, PII handling and retention under US state privacy laws, and periodic reviews of who - and which AI tools - can see what. - The governance system itself - the documentation, ownership models, and review loops that keep all of the above running without heroics. Qualifications - Governance is your craft, not your chore. You genuinely enjoy making data systems trustworthy and tidy. - AI-native. You use AI tools (Claude Code, Copilot, ChatGPT) daily to build quality checks, write automation, triage anomalies, and document as you go. - A hands-on senior operator. You write the SQL, debug the Airflow DAG, and configure the permissions yourself. - Automation-first. Your instinct for any recurring check is to build a monitor, not a checklist. - An enforcer people actually like. You'll hold engineers and analysts you don't manage to standards. Requirements - Zero pipeline incidents from unannounced source-data changes. - Zero freshness incidents - stakeholders never open a stale dashboard. - Every area of the business manages on official, well-maintained metrics and dashboards. - Every Segment event has an owner and a standard. - Governance runs as a system - documented processes that would survive you taking a month off. Benefits - Base salary: $75k–$100k/year - Equity: The whole company makes decisions on the data you'll guard. - Fully remote: This work needs deep focus, and we trust you to manage your environment. - Flexible PTO: We focus on results. Take what you need.

• This is a Data Engineer role embedded within a cross-functional product squad where data is treated as a product, not a service. • You will work closely with Product Managers, engineers and business stakeholders to shape how data supports customer experiences, decision-making and growth. • Alongside building reliable, scalable data pipelines, you will help define metrics, translate product needs into data models, and ensure data is accessible, reliable and actionable. • This role is ideal for someone who enjoys combining technical depth with strong collaboration and a sense of ownership over outcomes. • We operate a technology stack focused on Azure, leveraging Azure Data Factory and SQL databases to build the data warehouse. To construct the data warehouse that generates business value, you will collaborate with cross-functional squads and work closely with stakeholders to define data requirements, deliver solutions and maintain best practices. • Key responsibilities: • Partner with Product Owners and stakeholders to translate business and product requirements into well-defined data models, pipelines and metrics. • Design and develop scalable data pipelines, leveraging Azure Data Factory and other native Azure tools. • Own the data layer within your product area, ensuring data is reliable, well-modeled and fit for decision-making and product use cases. • Collaborate with team members to optimize data architecture, ensuring high performance and reliability. • Work with stakeholders to gather and refine data requirements, delivering solutions that align with business priorities. • Implement and maintain version control practices (e.g., Git), facilitating collaboration and ensuring code quality. • Maintain clear and comprehensive documentation of data workflows and processes. • Coordinate task progress using Jira, tracking and prioritizing workload while working with cross-functional teams. • Contribute to the development and enforcement of data engineering standards, best practices and documentation processes. • Actively participate in squad rituals (planning, refinement, retrospectives), contributing to prioritization and delivery decisions. • Assist in monitoring, troubleshooting and improving data pipelines and ETL processes to support continuous improvement efforts. • Stay up to date with industry trends and technologies to drive continuous improvements.

Role Description We are seeking a highly skilled and motivated Senior Data Analysis Engineer for our large-scale AI model and software evaluation framework – Ground Truth Regression. The ideal candidate will have a strong background in data engineering, machine learning principles, and statistical analysis. At Motional, large scale orchestration for data analysis plays a critical role in delivering our ML-centered autonomous driving vehicle. Our robo-taxi ML Models and software are developed by hundreds of developers and deployed multiple times a day. The Ground Truth Regression team provides the large scale perception analysis framework for validating all changes to perception which can impact the autonomous vehicle behavior. The GTRegression team validates the end impact of ML and software changes to the autonomous vehicle while those changes are in development, providing the tools for Root Cause Analysis and safety consideration. We monitor for model errors, anomalies, rare objects & long-tail driving scenarios across thousands of driving hours. The team develops the full stack – AWS Kubernetes orchestration framework, ReSimulation, model metrics, regression reports, and deep dive analysis UIs. The team works together with all other perception and ML teams to develop the metrics and analyze results. What You'll Do: - Work with ML Engineers and Autonomy Software Developers to develop new data analysis metrics and KPIs for the validation of the autonomous vehicle performance. - Drive innovation by researching and developing new large scale data analysis systems. - Own large-scale data analysis workflows that surface unexpected impacts of planned and unplanned changes to the ML models and software. - Build high-quality datasets to improve ML products through training & edge case validation. - Provide statistical depth on model performance & generalization through rigorous error and change analysis. Qualifications - Bachelor's or Master's degree in Computer Science, Data Science, or a related field. - Strong programming skills in Python, SciPy, and related data analysis frameworks. - Experience in machine learning, data mining, and statistical analysis. - Experience with large-scale data processing and distributed computing technologies. - Strong communication and software development skills: goal setting, design documentation, test frameworks, collaborative cross team development. Requirements - Ph.D. in Computer Science or a related field (Bonus Points). - Experience with cloud platforms such as AWS, Google Cloud, or Azure (Bonus Points). - Experience with machine learning in the autonomous driving domain (Bonus Points). - Publications or contributions to the AI/ML community (Bonus Points). Benefits - Motional’s benefits include but are not limited to medical, dental, vision, 401k with a company match, health saving accounts, life insurance, pet insurance, and more. Salary Range $159,000 — $207,000 USD Company Description Motional is a driverless technology company making autonomous vehicles a safe, reliable, and accessible reality. We’re driven by something more. - Our journey is always people first. - We aren't just developing driverless cars; we're creating safer roadways, more equitable transportation options, and making our communities better places to live, work, and connect. - Our team is made up of engineers, researchers, innovators, dreamers, and doers, who are creating a technology with the potential to transform the way we move. - We’re creating first-of-its-kind technology that will transform transportation. - To do so successfully, we must design for everyone in our cities and on our roads. - We believe in building a great place to work through a progressive, global culture that is diverse, inclusive, and ensures people feel valued at every level of the organization. - Diversity helps us to see the world differently; it’s not only good for our business, it’s the right thing to do. - Our team is behind some of the industry's largest leaps forward, including the first fully-autonomous cross-country drive in the U.S, the launch of the world's first robotaxi pilot, and operation of the world's longest-standing public robotaxi fleet. - We’re driven to scale; we’re moving towards commercialization of our technology, and we need team members who are ready to embrace change and challenges. - Formed as a joint venture between Hyundai Motor Group and Aptiv, Motional is fundamentally changing how people move through their lives. - Headquartered in Boston, Motional has operations in the U.S and Asia.

Role Description Estamos buscando uma pessoa para fazer parte deste time que vem crescendo a cada dia, tanto em número quanto em desenvolvimento. Se você é essa pessoa, que tem um senso de equipe e que procura por desenvolvimento constante, a BIX é para você! Qualifications - Experiência sólida no provisionamento e uso de serviços AWS como DMS, S3, Glue, Athena, Kinesis, IAM (roles/policies), VPC/networking, etc. - Experiência no desenho de pipelines near-real-time, semantics de processamento (at-least-once / exactly-once) e trade-offs entre latência e consistência. - Experiência com captura contínua de mudanças via redo logs, supplemental logging e replicação incremental. - Experiência sólida e comprovada em produção com Apache Kafka. - Domínio de Spark, Structured Streaming, Delta Lake e arquitetura lakehouse (camadas bronze/prata/ouro), Unity Catalog, lineage e data quality frameworks no Databricks. - SQL avançado e modelagem de dados relacional e dimensional. - Inglês avançado/fluente para atuação internacional. Requirements - Orquestração: Airflow, Data Factory ou equivalente. - Kafka Connect / Debezium: conectores de CDC integrados ao Kafka. - Certificação nas clouds (AWS, GCP, Azure). - Certificação em ferramentas de Engenharia de Dados (Databricks, Clickhouse, Airflow, dentre outras). Responsibilities - Projetar e implementar a migração de pipelines batch legados para uma arquitetura de streaming de dados em tempo real. - Configurar e operar captura de mudanças (CDC) na origem, replicando dados via AWS DMS para as camadas de streaming e lakehouse. - Modelar tópicos, partições e estratégias de serialização no Kafka, garantindo ordering, throughput e tolerância a falhas. - Desenvolver pipelines de ingestão e processamento no Databricks (Structured Streaming, Delta Lake), atendendo também demandas correntes da plataforma. - Garantir qualidade, observabilidade e governança dos dados em trânsito e em repouso. - Documentar arquitetura, decisões técnicas e realizar code review com o time. - Realizar reuniões com clientes, por meio de videoconferência, desenvolvendo os questionamentos estratégicos voltados ao seu negócio/processo, a cada início de projeto ou sprint. - Gerenciar atividades do projeto, utilizando métodos ágeis, de modo a garantir as entregas, a cada novo projeto. Benefits - Trabalho 100% remoto. - Plataforma de terapia e nutricionistas online. - Plataforma de bem estar. - Plano de carreira acelerado. - Contato direto com líderes de grandes empresas. - Ambiente diferenciado (Hora BIX, integrações e Happy Hours, Programa de Mentoria, Reuniões 1on1). - Aulas on-line de inglês (cambly). - Folga no aniversário.