• Work on building and evolving the organization’s analytical layer, transforming raw data into reliable, documented, and reusable data products. • Be responsible for structuring analytical models, Data Marts, and corporate metrics that support Business Intelligence, Analytics, and Machine Learning initiatives. • Ensure data quality, governance, scalability, and alignment with business needs. • Develop transformations and analytical models using DBT. • Build, maintain, and evolve analytical data layers, including Silver, Curated, and Gold. • Design and implement Data Marts oriented to business area needs. • Translate business rules and processes into scalable, standardized, and reusable data models. • Define, document, and govern corporate metrics, promoting consistency across teams. • Ensure data quality, traceability, observability, and reliability. • Develop automated tests and quality controls to validate data. • Support the provisioning of datasets for consumption by BI, Analytics, and Machine Learning teams. • Collaborate with Data Engineers, Data Scientists, BI Analysts, and business stakeholders. • Promote best practices in analytical modeling, documentation, versioning, and data governance. • Contribute to the evolution of analytical architecture and the organization of data layers. • Participate in defining standards and strategies for consuming corporate data. • Identify opportunities for optimization, performance improvements, and scalability of developed solutions.

Role Description Apartment List runs on data, and the Analytics Engineering team builds the internal data products that power decisions across Product, Marketing, Operations, Finance, and beyond. We hold ourselves to high standards through rigorous process, governance, and technical execution, and we form genuine partnerships with the teams we serve. This role is equal parts strategic thinking and hands-on execution. As a Lead Analytics Engineer, you'll be a technical anchor for the team: - Shaping architecture decisions - Mentoring engineers - Driving initiatives that improve both the health of our data systems and the business outcomes they enable You'll operate at company scope, influencing cross-functional roadmaps and setting the bar for what great analytics engineering looks like at Apartment List. Responsibilities - Design data solutions end-to-end: evaluate architecture options, articulate tradeoffs, and deliver production-ready, testable, maintainable code. - Influence and evolve the team's data architecture standards by researching, testing, and implementing modeling and tooling best practices, and analyzing the technical risks and long-term implications of key decisions. - Proactively find and execute on high-value tech health and business opportunities before they're on anyone's roadmap. - Actively participate in business measurement discussions, communicating both the value and structural limitations of proposed metrics. - Mentor AEs and analysts with targeted feedback and specific growth expectations; enable the broader team through documentation, process development, and knowledge sharing. - Contribute to roadmap and goal-setting as a decision-maker. - Communicate clearly with technical and non-technical audiences, anticipating blockers and scope changes before they require escalation. Qualifications - 7+ years in analytics engineering or a closely related data role, with a track record of end-to-end technical ownership across architecture, delivery, and long-term maintenance. - Expert SQL: performant at scale, structured for readability, and built to handle edge cases. - Advanced dbt, including building team practices around testing, documentation, and modularity. - Proven data architecture decisions with lasting structural impact. - Enabled others through mentorship, code review, process, or tooling. - Easily explain data architecture tradeoffs to both engineers and business stakeholders in the same conversation. - Deep ELT/ETL knowledge with strong instincts for observability and failure recovery. - CI/CD in a data context, including automated testing pipelines and deployment validation. - Bonus: Ownership of BI tools as a product, including semantic layer health and cross-team data model standards. - Bonus: 2+ years working with performance marketing and frontend eventing data. Compensation We carefully consider a variety of factors to determine compensation for each position, including the role, level, and work. The US Total Target Compensation (TTC) for this position is: - Tier 1: $165,000 to $200,000 (Base: $148,500 to $176,000) - Tier 2: $153,000 to $185,000 (Base: $137,700 to $162,800) - Tier 3: $141,000 to $170,000 (Base: $126,900 to $149,600)

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.