Role Description The Data Platform team builds and operates BILL’s core data infrastructure, providing the end-to-end foundation that collects, stores, processes, governs, and serves data so every team at BILL can use it. We own the full data stack: inbound and outbound data lake, real-time streaming pipelines, batch processing, and data access layers including a Starburst query engine, Databricks Feature Store, Neo4j Knowledge Graph, and OpenSearch. Some capabilities require real-time access with strict low-latency SLAs. Our charter is to simplify the data landscape and power AI at BILL. To achieve this, we focus on building scalable platform capabilities rather than creating one-off pipelines or analytics reports. Engineers here work at the systems level: designing architectures, incubating new capabilities, setting standards, and enabling the rest of BILL to self-serve. The team sits within the CTO organization. Responsibilities - Operate at the architectural level, driving platform-wide technical decisions and mentoring the team. - Own and evolve critical infrastructure across the full data lifecycle, spanning ingest, store, enrich, query, and serve. - Architect and own critical data platform capabilities end-to-end, from inbound ingestion through data lake storage to downstream serving, including the feature store, query engine, knowledge graph, and search. - Define technical direction for the team’s most complex, cross-cutting problems, such as streaming versus batch trade-offs, schema contracts, data access patterns, and real-time serving architectures. - Drive design and delivery of new capabilities from inception to GA, including reference implementations, SLAs, and clear ownership handoff models. - Establish and maintain architectural standards and engineering patterns adopted across the organization. - Lead multi-phase technical migrations at enterprise scale, including compute platform upgrades, warehouse-to-lake migrations, and infrastructure modernization. - Partner with engineering teams across BILL, such as ML/AI, Risk, Payments, and Analytics, to translate diverse data needs into durable platform solutions. - Mentor senior and staff engineers, actively shaping the technical culture and engineering quality bar of the team. - Own and continuously improve critical production systems with a focus on reliability, cost efficiency, and a self-serve developer experience. Qualifications - Bachelors degree in Computer Science, Engineering, Mathematics, or equivalent work experience. - 8+ years of experience in data engineering, distributed systems, or software engineering with a heavy focus on data infrastructure. - 5+ years of experience specifically on data platform, data infrastructure, or data systems teams, rather than purely analytics or BI roles. - Expertise in distributed systems design for data workloads, including a deep understanding of streaming, batch, and real-time serving trade-offs. - Hands-on experience with event streaming platforms (such as Kafka, Flink, Spark Streaming, or equivalent) and CDC-based ingestion patterns. - Strong proficiency with batch processing stacks (such as Airflow, dbt, Spark/Glue, or equivalent). - Experience with modern open table formats and data lake architectures, including Apache Iceberg, Delta Lake, or equivalent frameworks. - Familiarity with data access and serving layers, including query engines (Trino/Starburst, Presto), feature stores, vector stores, or graph databases. - Expert-level SQL and strong Python skills, backed by solid software engineering fundamentals such as CI/CD, testing, and observability. - Demonstrated experience architecting and operating large-scale, production-grade data platforms. - A proven track record operating as a technical lead, with the ability to drive ambiguous, high-impact projects from first principles to production. - The ability to define technical standards that hold across organizational boundaries rather than just within a single team. Desired Qualifications - 3+ years of experience in financial services, fintech, or SaaS companies. - Experience building self-serve data platforms or developer-facing infrastructure tooling. - Experience working in fintech, financial data, or highly regulated data environments. Benefits - 100% paid employee health, dental, and vision plans (choose HMO, PPO, or HDHP). - HSA & FSA accounts. - Life Insurance, Long & Short-term disability coverage. - Employee Assistance Program (EAP). - 11+ Observed holidays and wellness days and flexible time off. - Employee Stock Purchase Program with employee discounts. - Wellness & Fitness initiatives. - Employee recognition and referral programs. - And much more.

• Design and deploy high-performance, distributed web scrapers using Python and Scrapy • Utilize Browser Scripting tools to navigate, interact with, and extract data from websites • Deploy, scale, and manage scraping workloads on Kubernetes • Define strict JSON Schemas and leverage Pydantic for data validation • Build and optimize search and storage pipelines using Elasticsearch • Architect robust pipeline workflows to manage the end-to-end data lifecycle • Manage complex proxy rotation and session handling to maintain high success rates

Role Description Estamos em busca de uma pessoa Engenheira de Dados Sênior para atuar em um sustetanção de projetos de dados. Procuramos um(a) profissional com forte conhecimento técnico, perfil proativo, autonomia e capacidade analítica para atuar na evolução de soluções existentes e na melhoria contínua dos processos. Mais do que executar demandas, buscamos alguém que questione, proponha melhorias e contribua ativamente para sustentação. - Atuar na sustentação e evolução de pipelines e soluções de dados; - Modernizar processos e fluxos atualmente suportados por tecnologias legadas; - Desenvolver e otimizar rotinas de processamento e transformação de dados; - Modelar estruturas de dados utilizando modelagem dimensional; - Identificar oportunidades de melhoria de performance, qualidade e governança dos dados; - Trabalhar de forma colaborativa com times multidisciplinares na definição e implementação de soluções técnicas; - Garantir a estabilidade, confiabilidade e eficiência dos ambientes de dados. Qualifications - Experiência sólida como Engenheiro(a) de Dados Sênior; - Domínio de Databricks; - Experiência avançada com Python; - Conhecimento em Modelagem Dimensional; - Experiência FORTE com bancos de dados, especialmente MongoDB; - Forte capacidade analítica, autonomia e perfil voltado para resolução de problemas. Requirements - Experiência com banco de dados Oracle legado; - Vivência em projetos de modernização de ambientes legados; - Experiência em sustentação e melhoria contínua de plataformas de dados. Benefits - Proativa e com senso de dono; - Dinâmica e orientada à solução de problemas; - Comunicativa e colaborativa; - Capaz de trabalhar com autonomia e tomada de decisão; - Comprometida com qualidade técnica e melhoria contínua.

Role Description This is a hands-on engineering role — you'll be writing production Python and SQL, building Airflow DAGs, and contributing to our Data Vault 2.0 warehouse alongside a team of staff engineers. - Develop complex and efficient data pipelines to transform raw data sources into reliable, well-tested components of our data models. - Design, build, and maintain data pipelines that deliver accurate, trusted data with the freshness our stakeholders depend on. - Make data modeling decisions within our Data Vault 2.0 warehouse that balance raw fidelity in the vault with the consumption patterns of downstream marts and analytics. - Administer and optimize Snowflake across warehouse sizing, query performance, access controls, RBAC and user/role management, and ongoing cost tuning. - Build and maintain resilient Airflow DAGs and CI/CD pipelines that make deployments predictable, repeatable, and safe to roll back. - Implement automated testing (unit, integration, and data quality) so issues are caught in CI before they reach production. - Own production observability through our internal tooling, tuning alerts, responding to incidents, and closing the loop from production issues back into pre-load validation and CI checks. - Leverage AI-powered tools (e.g., Claude Code, Cursor, Snowflake Cortex) as a core part of your development workflow to accelerate code generation, automate documentation, and improve code quality. - Work with stakeholders across Executive, Product, Engineering, and business teams to translate the "why" behind a request into a technical solution that meets the business need. - Lead technical projects end-to-end, scoping with stakeholders, documenting requirements, and explaining technical trade-offs without relying on a Product Manager. - Drive cross-team initiatives that require influence and alignment to achieve a common goal. - Drive best practices for data governance, privacy, and security, including the change management and validation discipline required for SOX-relevant reporting. - Take an active part in the operational responsibilities of running our data infrastructure, with a focus on reliability, cost efficiency, and observability. Qualifications - Minimum 5 years experience as a software engineer or data engineer with a focus on data systems. - Advanced proficiency writing complex SQL and manipulating large structured and semi-structured datasets. - Proficiency in Python for building production-grade data pipelines. - Hands-on Snowflake administration experience, including warehouse management, RBAC and role design, access controls, and cost governance. - Demonstrable experience designing and implementing modern data warehouses, with an understanding of best practices. - Experience modeling data in cloud data warehouses such as Snowflake, Redshift, or BigQuery. Data Vault 2.0 experience strongly preferred, deep dimensional modeling experience acceptable with willingness to learn Data Vault. - Demonstrated experience using testing frameworks to validate data and code in a production environment. - Hands-on experience with data observability tooling. - Proficiency using AI coding assistants (e.g., Claude Code, Cursor, Snowflake Cortex) as a core part of your development workflow. - Track record of leading technical projects end-to-end without a Product Manager — from scoping through documentation through stakeholder communication. - Comfort working in a regulated environment (e.g., SOX-relevant financial reporting). - Willingness to develop deep domain expertise in insurance and the Pie business over time. Requirements - The best data engineers here understand premium, loss, and policy lifecycle as well as they understand Snowflake. Company Description Pie Insurance is a technology-driven small business insurance company focused on making commercial insurance simpler, smarter, and more affordable. Our Data Engineering team sits at the intersection of technology and business operations, and this role is a critical part of how we scale. This is a contract role. The position is fully remote and based in Brazil. Candidates must be authorized to work as contractors in Brazil.