McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve – we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow’s health today, we want to hear from you. We are seeking a Senior Data Engineer / Software Developer to support and scale our AI‑driven data platforms. This role focuses on building robust AI pipelines, shared tooling, and engineering standards that enable consistent, high‑quality generation and consumption of advanced analytics outputs across the organization. The engineer will play a key role in strengthening the technical foundation that supports analytics and oncology insights. Key Responsibilities Business Drivers - Collaborate with data scientists, machine learning engineers, and analytics teams to provide technical direction for AI and advanced analytics platforms - Work closely with data warehousing, data engineering, and cloud platform teams to design optimal architectures for AI‑driven data solutions - Enable the scalable use of AI‑generated outputs (e.g., ML predictions, extracted signals, model outputs) in conjunction with structured data to support analytics and oncology insights - Partner with senior management and stakeholders to communicate AI system capabilities, implementation approaches, assumptions, and limitations in clear, non‑technical language Technical Responsibilities - Participate in the full lifecycle of AI and data platform solutions, including planning, design, implementation, deployment, monitoring, and ongoing maintenance - Design, build, and maintain production‑grade AI pipelines, shared frameworks, and supporting services in the cloud (e.g., AWS, GCP, Azure; Azure preferred) - Design, test, and maintain AI‑enabled applications and services using modern software engineering and testing methodologies - Perform code reviews and help define engineering and AI code standards to ensure high‑quality, scalable, and maintainable solutions - Develop and maintain scalable data and AI pipelines using Python and supporting technologies - Design and implement data architectures that support downstream analytics and access by McKesson analysts and AI data consumers Drive innovation - Develop reusable engineering solutions to support AI workloads, model execution, inference pipelines, and integration into downstream data products - Evaluate new AI‑related tools, frameworks, and platforms to improve scalability, reliability, and developer productivity prior to broader adoption - Experience supporting AI or machine learning solutions in healthcare, oncology, genomics, or medical data domains is preferred but not required Minimum Requirement - Degree or equivalent and typically requires 7+ years of relevant experience. Education - A degree in a quantitative field such as Statistics, Machine Learning, Mathematics, Computer Science, Economics, Epidemiology or any other related field - Master’s Degree or higher preferred Critical Skills - 3+ years of relevant experience in data engineering or software development roles supporting analytics or AI‑enabled solutions; healthcare experience preferred - Proficiency in Python and SQL, with demonstrated experience developing and maintaining reliable, production‑grade data pipelines and analytical datasets - Experience building and supporting internal tools or applications used for data validation, monitoring, review, or operational analytics workflows - Working knowledge of application integration patterns, including service‑based architectures and data access layers that support UI‑driven tools - Hands‑on experience using Databricks for data processing, analytics development, and collaboration with data science or analytics teams - Experience working within Microsoft Azure environments, applying standard engineering practices to deliver maintainable, well‑documented solutions Additional Skills - Familiarity with machine learning or AI concepts, including model lifecycles, inference workflows, and integration of model outputs into analytics or data products - Exposure to Natural Language Processing or other unstructured data workflows, such as text ingestion, extraction, or downstream signal consumption - Experience with NoSQL or semi‑structured data stores and alternative data persistence patterns - Experience with analytics visualization tools or reporting solutions, and familiarity with modern scripting or web technologies used to support internal tools We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here. Our Base Pay Range for this position $136,300 - $227,100 McKesson has become aware of online recruiting-related scams in which individuals who are not affiliated with or authorized by McKesson are using McKesson’s (or affiliated entities, like CoverMyMeds or RxCrossroads) name in fraudulent emails, job postings or social media messages. In light of these scams, please bear the following in mind: McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application. McKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates. McKesson job postings are posted on our career site: careers.mckesson.com. McKesson is an Equal Opportunity Employer McKesson provides equal employment opportunities to applicants and employees, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, genetic information, or any other legally protected category. For additional information on McKesson’s full Equal Employment Opportunity policies, visit our Equal Employment Opportunity page. McKesson is committed to being an Equal Employment Opportunity Employer and offers opportunities to all job seekers including job seekers with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, please contact us by sending an email to (United States) Disability_Accommodation@McKesson.com or (Canada) Accessibility@mckesson.ca. Resumes or CVs submitted to this email box will not be accepted. Join us at McKesson!

Nosso Modo de Fazer no Time: Transforme sua carreira com o iFood! Somos uma empresa brasileira de tecnologia referência na América Latina. Por meio de soluções inovadoras, conectamos milhares de restaurantes a milhões de consumidores diariamente com uma média de 100 milhões de pedidos mensais. Além do delivery de comida, também somos Mercado, Farmácia e Pet. Temos também o iFood Pago, nossa Fintech, que engloba o iFood Benefícios, o vale alimentação e refeição do iFood e o próprio iFood Pago, o banco do restaurante. Junte-se a nós e faça parte de uma equipe que está sempre à frente com tecnologia de ponta e inovação constante. Você fará parte do time de Governança de Dados, com o desafio de melhorar a descoberta de dados, assim como toda a camada de metadados e ciclo de vida dos dados que temos, definindo e construindo o modo de trabalhar com dados aqui dentro, colaborando ativamente com times de engenharia, arquitetura e dados para garantir que tenhamos dados confiáveis, documentados, seguros e acessíveis. Esta é uma posição com componente técnico em desenvolvimento e foco em identificar oportunidades e aplicar boas práticas de governança em ambientes modernos de dados em nuvem. Seu Cardápio Diário: - Definirá políticas e processos de governança de dados (catalogação, linhagem, entre outros); - Ajudará a projetar e construir soluções que facilitem a aderência às políticas definidas; - Participará da definição e evolução do framework de governança, promovendo a cultura de dados entre os times; - Apoiará iniciativas de novas tecnologias e modos de trabalhar com dados para garantir que surjam com governança definida, ajudando domínios de dados a adotarem práticas de ownership e interoperabilidade; - Irá desenvolver monitores e indicadores de governança; - Colaborará com arquitetos e engenheiros para garantir que os pipelines de dados estejam em conformidade com as políticas definidas; - Traduzirá requisitos de governança em soluções técnicas alinhadas às boas práticas de engenharia e realidade do negócio. Ingredientes Que Buscamos: - Experiência prévia com governança de dados (Data Governance, Data Stewardship, Data Quality, Metadata Management etc.); - Capacidade de adequar a teoria de governança para a realidade da operação do dia a dia; - Experiência em engenharia de dados ou software (construção de pipelines de dados, micro serviços, git, testes, modularização e orquestração); - Sólido entendimento do ciclo de vida de dados e de princípios de arquitetura moderna (Data Lake, Lake house, Data Warehouse); - Conhecimento em cloud (AWS, GCP ou Azure); - Capacidade de escrever e interpretar queries em SQL e scripts em Python para análise, transformação e validação de dados; - Boa comunicação com públicos técnicos e não técnicos; habilidade de traduzir complexidade técnica em linguagem acessível; - Experiência com gestão de metadados, linhagem de dados, catalogação e classificação - Mindset colaborativo, resiliente e voltado à melhoria contínua; Para realçar o sabor: - Conhecimento ou certificações em frameworks de governança de dados (ex: DAMA-DMBOK, DCAM, EDM Council); - Experiência com ferramentas de monitoramento e visualização de dados; - Experiência com ferramentas de catálogo como DataHub e OpenMetadata; - Conhecimento em Data Warehouse, Bancos de dados e Kafka; - Conhecimentos de ferramentas como Airflow, Spark, Databricks, DBT ou similares; Buscamos uma pessoa apaixonada por inovação e tecnologia, que esteja sempre em busca de novos aprendizados e que goste de desafios. Se você se identifica com este perfil, adoraríamos conhecer você!

• Support the Bank’s “People First” focus and rules of engagement—maintaining a professional demeanor, working as an active member of the CNOB team, providing clients excellent service, always striving to make CNOB “A Better Place to Be” • Architect, build, and maintain data pipelines using Apache Airflow, ensuring seamless data movement across AWS S3 and Amazon Redshift. • Design and implement scalable data models using dbt Labs, transforming raw data into clean datasets optimized for analytical querying. • Facilitate the setup of new data sources, designing the architecture to securely and efficiently ingest data from third-party APIs, transactional databases, and external platforms into the data lake/warehouse. • Maintain a deep understanding of the underlying data model to troubleshoot bottlenecks, optimize Redshift query performance, and implement rigorous data quality testing and alerting. • Serve as a subject matter expert on data architecture, establishing best practices for code reviews, version control, and CI/CD workflows within the data engineering team.

Role Description We’re hiring a Staff Data Engineer to design and build robust data pipelines for our corporate Data Lake. In this role, you’ll own data products end-to-end in production, work autonomously on significant projects, and mentor junior engineers. You’ll be expected to make sound data engineering trade-offs under limited supervision, with strong hands-on experience in Spark, Databricks, Delta Lake, and Airflow. At Pismo, the Data Lake team is responsible for centralizing and organizing data into a single, trusted platform that supports decision-making across the company and for external clients. We work on challenges such as: - Scaling global data infrastructure - Delivering high-quality reporting - Enabling secure, self-service access to data Key responsibilities include: - Design and implement data ingestion and transformation pipelines (batch and near-real-time) using PySpark/SparkSQL on Databricks. - Own data pipelines end-to-end in production: freshness, correctness, availability, and SLA adherence. - Build and maintain Delta Lake tables following medallion architecture patterns (bronze/silver/gold). - Design and optimize Airflow DAGs (MWAA) for complex orchestration scenarios. - Implement and maintain data quality frameworks (Great Expectations or equivalent) as integrated pipeline gates. - Write advanced SQL for data modeling, transformation, and performance optimization. - Conduct thorough code reviews and mentor Analyst-level engineers through pairing and design guidance. - Investigate, diagnose, and resolve data quality incidents and pipeline failures independently. - Collaborate with Analytics, BI, and Product teams to design consumer-friendly datasets. - Contribute to CI/CD, testing standards, and data governance practices. This is a remote position. A remote position does not require job duties to be performed within proximity of a Visa office location. Remote positions may be required to be present at a Visa office with scheduled notice. #LI-Remote Qualifications - Apache Spark (PySpark, SparkSQL) — production experience - Databricks (jobs, workflows, cluster management, tuning) - Delta Lake (ACID tables, OPTIMIZE, VACUUM, schema evolution, MERGE) - Advanced SQL (window functions, CTEs, query optimization) - Apache Airflow / MWAA (DAG design, retries, backfills, SLAs) - Amazon S3 data lake design (partitioning, layout, lifecycle) - Data quality frameworks (Great Expectations or equivalent) - Data modeling (dimensional / Kimball, medallion layers) - Git/GitHub, CI/CD for data pipelines - Terraform - Python for automation and data processing Requirements - Desirable qualifications: - CDC patterns (DMS, incremental processing, MERGE upserts) - Streaming ingestion (Structured Streaming, Auto Loader) - AWS Glue Catalog / Unity Catalog - Metadata management (OpenMetadata) - BI integration (Superset, dashboarding) Company Description Founded by experienced entrepreneurs and engineers in 2016, Pismo is a technology company that provides a comprehensive processing platform for banking, card issuing, and financial market infrastructure and helps customers innovate and build the next generation of banking and payment solutions. Pismo joined Visa in 2024. Leveraging Visa’s solutions, our core platform, and an expanding suite of capabilities, Pismo addresses the technological challenges that large banks, marketplaces, and fintech companies face in migrating from legacy systems to more advanced technology in the market. Pismo’s cloud-based platform empowers firms to build and launch financial products rapidly, scaling as they grow to have a broader audience while keeping high security and availability standards. Pismo’s 500+ employees are located in more than 10 countries around the world.