• Assist in building and maintaining batch and streaming data pipelines using tools such as Spark, Databricks, Snowflake, and cloud-native services. • Support the development of ETL/ELT workflows using orchestration tools like Apache Airflow, dbt, or managed cloud schedulers. • Help ingest structured and semi-structured data from sources such as S3, ADLS, GCS, APIs, or Kafka into raw and curated data layers. • Write and maintain SQL and Python-based transformations for cleaning, joining, and aggregating datasets. • Participate in implementing data quality checks, validation rules, and basic monitoring to ensure data accuracy and reliability. • Collaborate with data engineers, analysts, and data scientists to understand how datasets are consumed by analytics models and AI agents. • Assist in preparing datasets and feature tables that can be used by AI/ML pipelines or autonomous agents for decision-making and automation. • Explore how AI agents can interact with data platforms (e.g., querying data, triggering pipelines, summarizing results) under guidance from senior team members. • Contribute to documentation of data flows, schemas, and pipeline logic to support team knowledge sharing. • Learn and follow data modeling, governance, and privacy best practices, especially in regulated or privacy-conscious environments. • Support version control and deployment processes using Git and basic CI/CD workflows.

Our Company We care about helping people. Our purpose is to provide help and inspire confidence in our clients and communities everywhere. Our associates feel a sense of belonging in an inclusive place with an amazing history and a sharp focus on our future. Our connected culture is who we are and how we work together to achieve our strategies, accelerate our transformation, and achieve extraordinary results. It’s an exciting time to be a part of H&R Block! What you'll do... We’re seeking a Senior Data Engineer with strong experience building and evolving modern data- and AI-enabled platforms. This is a hands-on, senior individual contributor role focused on designing, developing, and optimizing scalable software solutions across data, analytics, and AI workloads. You will play a key role in implementing cloud-native data and AI services, contributing to architectural decisions, and partnering closely with data engineers, ML engineers, and product teams to deliver reliable, secure, and high-performing platforms. While not a people manager, you’ll be expected to provide technical leadership through influence, code quality, and design rigor. Day to day, you'll... Design & Build - Design, develop, and maintain scalable software components for data and AI platforms using Microsoft Fabric, Azure Data Services, Data Lakes, and cloud-native services. - Build robust data ingestion, transformation, and serving layers that support analytics, AI, and operational use cases. Implement End-to-End Solutions - Contribute to end-to-end delivery of data and AI solutions, including data pipelines, semantic models, APIs, and consumption layers. - Ensure solutions meet standards for performance, reliability, security, and cost efficiency. Modernize & Optimize - Participate in modernizing legacy systems toward cloud-native and AI-enabled architectures. - Optimize pipelines, queries, and services for scale, resilience, and operational excellence. Enable Data & AI Products - Partner with analytics, AI/ML, and application teams to deliver reusable data assets, shared services, and self-service capabilities. - Support downstream use cases such as dashboards, advanced analytics, and machine learning workloads. Engineering Excellence - Apply and promote best practices for software engineering, CI/CD, testing, observability, and infrastructure-as-code. - Contribute to standards for data quality, data governance, lineage, security, and monitoring (e.g., DataDog). - Data governance, risk management, and compliance using Microsoft Purview Technical Collaboration & Mentorship - Provide technical guidance and code reviews for peers and junior engineers. - Collaborate closely with product managers, architects, and cross-functional partners to deliver high-impact solutions. What you'll bring to the team... - Bachelor’s degree in a related field or the equivalent through a combination of education and related work experience. - 5+ years of experience building production-grade software, with significant exposure to data platforms, cloud services, or AI-enabled systems. - Strong hands-on experience with Azure data technologies and modern data architecture patterns. - Proficiency in SQL, with solid software engineering fundamentals (data modeling, APIs, testing, version control). - Experience with CI/CD pipelines, DevOps practices, and cloud infrastructure. - Ability to design systems, reason about trade-offs, and contribute to architectural discussions. - Strong communication skills and the ability to work effectively with cross-functional teams. - A growth mindset and passion for learning, quality engineering, and continuous improvement. It would be even better if you also had... - Experience with Microsoft Fabric. - Experience with OneLake, Lakehouse architecture, Power Platforms, or Fabric-native governance tools. - Exposure to feature stores, or advanced analytics. - Azure or Microsoft Fabric certifications. Why work for us Since 1955, we have been leaders in tax preparation, financial services, and small business solutions. With 70,000 associates and 9,000 retail tax locations across North America, Australia, Ireland, and India, we have helped millions of clients and countless communities. If you embrace challenges as opportunities, value winning as a team, and seek to make a meaningful difference, join us on our journey. You’ll reap the rewards of helping others along with competitive compensation and benefits to support your health and well-being. Specific benefits may vary based on your role. For detailed eligibility requirements and benefits information, visit blockbenefits.com. Equal Opportunity Employer: H&R Block does not tolerate discrimination based on a person’s race, color, religion, ancestry, age, sex/gender (including pregnancy, childbirth, related medical conditions and sex-based stereotypes and transgender status), sexual orientation, gender identity or expression, service in the Armed Forces, national origin, physical or mental disability, genetic information, citizenship status or any other status protected by law. Sponsored Job #LI-SH1 #LI-Remote

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, definindo e construindo o modo de trabalhar com dados aqui dentro do iFood, colaborando ativamente com times de engenharia, arquitetura e dados. Irá atuar próximo ao pilar de Data Quality & Data Reliability para garantir que tenhamos dados e demais artefatos confiáveis, precisos, completos e disponíveis para decisões de negócio assertivas. Esta é uma posição que exige conhecimento técnico em desenvolvimento e foco em identificar oportunidades, além de saber aplicar boas práticas de governança em ambientes modernos de dados em nuvem. Seu Cardápio Diário: - Definirá políticas e processos de governança de dados com foco em Data Quality (qualidade, confiabilidade, linhagem, legislação, entre outros); - Ajudará a projetar e construir soluções que facilitem a aderência às políticas estabelecidas, definindo dimensões de qualidade (acurácia, completude, consistência, atualidade, validade, unicidade) e métricas de representação para dados, pipelines, dashboards, dentre outros; - Participará da definição e evolução do framework de Governança, promovendo a cultura de dados e impulsionamento da disciplina de Data Quality 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, accountability e interoperabilidade; - Irá desenvolver monitores e indicadores de governança, como scorecards de confiabilidade dos ativos de dados, construindo meios para validação, testes automatizados e alertas para monitoramento contínuo de qualidade; - Desenvolver e disseminar o uso de bibliotecas reutilizáveis, data profiling e detecção de anomalias em escala; - Colaborará com arquitetos e engenheiros para garantir que os pipelines de dados estejam em conformidade com as políticas definidas; Ingredientes Que Buscamos: - Experiência prévia com governança de dados (Data Governance, Data Stewardship, Metadata Management, etc), principalmente com participação em iniciativas de Data Quality; - Capacidade de adequar a teoria de governança para a realidade da operação do dia-a-dia, com conhecimento profundo das dimensões de qualidade de dados e como aplicá-las na prática; - Vivência com monitoramento, alertas e troubleshooting de problemas de dados, com implementação de processos de Data Incident Response; - 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 e classificação de dados; - 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 prática implementando frameworks de Data Contracts, Data Quality e Observabilidade de Dados em ambiente produtivo (Great Expectations, Soda, dbt tests, Monte Carlo, Datafold ou similares); - Experiência com Machine Learning Ops, tendo familiaridade sobre conceitos como data drift, concept drift e model drift, e processos/ferramentas de validação de features, modelos e agentes de IA (TensorFlow Data Validation, Pandera, Pydantic, Deepchecks ou similares); - Experiência com ferramentas de monitoramento e visualização de dados; - Conhecimento em Data Warehouse, Bancos de Dados e Kafka; - Conhecimentos de ferramentas como Airflow, Spark, Databricks, dbt ou similares; - Conhecimento em estatística aplicada para detecção de anomalias e profiling. Buscamos uma pessoa apaixonada por tecnologia e governança de dados, que esteja sempre em busca de novos aprendizados e que goste de desafios. Se você se identifica com este perfil, adoraríamos conhecer você!

• Lead a small team of senior data engineers—breaking down ambiguous problems, removing blockers, and keeping engineers aligned on standards without micromanaging execution • Serve as the initial client-facing voice of the team, translating client needs into scalable solutions and supporting conversations around data access, architecture, and governance • Drive AI-powered development adoption across the team—tools like Claude or similar—to automate workflows, build agents, and improve efficiency while maintaining quality and architectural integrity • Partner with leadership to evaluate AI tools and platforms, including due diligence, testing, and ROI assessment • Guide data pipeline and platform architecture with an eye toward long-term scalability, not short-term fixes • Conduct code and pull request reviews to reinforce standards, quality, and consistency • Work cross-functionally to unblock data access, standardize data models, and align on a common data language • Translate strategy into clear execution plans—sequenced, resourced, and aligned to business priorities • Make and communicate tradeoff decisions on priorities, timelines, and architecture, surfacing risks and implications for leadership • Coach team members on how work is done, not just what gets delivered