• Develop and implement data pipelines (batch and streaming) using Dataflow (Apache Beam) • Design and optimize BigQuery-based data warehouses and data marts • Work on data ingestion, transformation, and loading (ETL/ELT) using GCP services • Collaborate with business teams to understand requirements and convert them into scalable data solutions • Ensure data quality, performance, and cost optimization across GCP workloads • Support data modeling activities (dimensional and normalized models) • Assist in solution design and architecture discussions • Troubleshoot pipeline performance issues and optimize processing • Contribute to CI/CD and deployment automation for data pipelines • Work within agile teams and support sprint-based delivery

• Lead data engineering delivery to build reliable, scalable pipeline leveraging Snowflake, Sigma and other applications to support complex data analytics and self-service reporting. • Drive integration with popular tools and services in the broader data warehousing ecosystem. • Optimize architectural performance across the Data stack by refining execution parameters and enforcing best practice strategies to maximize resource efficiency and throughput. • Establish and evolve a data governance operating model across data quality, lineage, metadata management, stewardship, cataloging, and lifecycle management. • Perform advanced code-level analysis and resource profiling to identify and mitigate systemic root causes, ensuring the stability and reliability of high-scale production workloads. • Coordinate with internal stakeholders to ensure effective communication, issue resolution, and on-time delivery of projects. • Participate in weekend and weekday on call rotation.

• Owning and evolving the end-to-end data architecture, aligned to Cooper Parry’s strategy and enterprise standards • Defining architectural patterns, principles, and standards for: Data ingestion, Data transformation, Data modelling, Analytics and reporting • Acting as a trusted advisor to senior stakeholders on data architecture decisions • Designing and oversee solutions using Microsoft Fabric, including: OneLake Lakehouse and Warehouse architectures, Data Pipelines, Spark / Notebooks, Semantic models • Ensuring platform scalability, performance optimisation, cost efficiency, and security • Guiding the transition from legacy platforms where applicable • Providing technical leadership across data engineering practices, including: Batch and streaming data pipelines, ELT/ETL frameworks, CI/CD for data platforms • Architecting robust integrations from internal and third-party systems • Defining data quality, validation, and monitoring standards • Defining and enforcing: Data governance frameworks, Master and reference data strategies, Access control and role-based security • Ensuring compliance with GDPR and organisational security standards • Promoting high data quality and lineage transparency • Working closely with: Data Engineers, Analytics Engineers, BI Developers, Product and Business stakeholders • Mentoring and upskill team members • Reviewing solution designs and providing constructive architectural guidance

Role Description Buscamos um desenvolvedor Python com sólida experiência em dados que saiba escrever código de produção — APIs, pipelines, integrações — e que entenda profundamente o dado que trafega por ele. Buscamos quem já viveu os dois lados e se move entre eles com naturalidade. O time trabalha com um fluxo completo de dados: - Extração de bases relacionais, processamento em camadas na AWS (arquitetura medalhão) e disponibilização para microsserviços e BI. - A pessoa precisará atuar com autonomia, propor o melhor caminho técnico e transitar entre diferentes stacks conforme a demanda do projeto. Responsibilities - Ingestão e Extração: Desenvolver scripts (Python puro ou PySpark) para consultar bases SQL Server, tratar os dados e publicá-los em buckets AWS S3. - Processamento (Arquitetura Medalhão): Construir pipelines para mover e refinar dados entre as camadas Bronze e Prata utilizando Python, PySpark e AWS Glue. - Disponibilização de Dados: Garantir a entrega de dados em OpenSearch (Fast Data) e no Amazon Redshift (para suporte ao time de BI). - Integração com Microsserviços: Colaborar com a camada de consumo, onde microsserviços em Node.js consultam o OpenSearch para servir as aplicações finais. - Visão Consultiva: Atuar de forma autônoma, indicando os melhores caminhos técnicos e arquiteturais para o projeto. - Criar e consumir APIs dentro do ecossistema AWS (Lambda, Glue, S3). Qualifications - Python sólido: código estruturado, orientado a objetos e com testes (Pytest ou similar). - Experiência real com pipelines de dados: PySpark, AWS Glue, Mage.ai ou equivalente. - AWS na prática: S3, Lambda, Redshift, Glue. - Vivência com microsserviços e APIs — criação, integração e arquitetura. - SQL e integração com bases relacionais (SQL Server, PostgreSQL ou similar). - CI/CD e versionamento: GitHub Actions, GitLab, Terraform ou equivalente. - Perfil adaptável: confortável em trabalhar com diferentes stacks conforme o projeto exige. Differentials - Node.js — o microsserviço de consulta de dados do projeto está em Node. - Apache Kafka ou experiência com mensageria assíncrona. - OpenSearch ou Elasticsearch. - Atuação em contextos de alta criticidade ou volume de dados. Benefits - Esta contratação será no modelo PJ, atuando no modelo remoto.