About Us:

At CI&T, we leverage cutting-edge technology and data-driven innovation to create solutions that help our global clients thrive in their industries. We are seeking a skilled and motivated Data Architect to design and implement scalable, secure, and efficient data solutions that empower advanced analytics and machine learning capabilities. If you are passionate about working with cloud-based technologies such as Azure Synapse, PySpark, and Azure Data Lake with Delta, we want you on our team.

Responsibilities:

- Collaborate with business stakeholders to gather data analysis and usage requirements, translating them into robust, scalable, and efficient technical solutions.

- Architect, design, and implement Azure Data Lake solutions with Delta Lake for reliable and performant data storage and management.

- Develop and maintain complex ETL pipelines using Azure Synapse to run PySpark jobs for near real-time data ingestion, transformation, and enrichment.

- Design scalable database structures (both SQL and NoSQL) to optimize data retrieval and support large-scale, near-real-time workloads.

- Monitor and optimize the performance of Azure Synapse pipelines and Azure Data Lake solutions to ensure efficient and reliable data access.

- Conduct data profiling and data quality assessments, identifying issues and implementing solutions to improve data accuracy, consistency, and reliability.

- Define and enforce Data Governance policies to ensure compliance with regulatory - requirements, data security, and privacy standards.

- Create and maintain clear and detailed data flow diagrams, data mappings, and technical documentation to support data integration and architecture efforts.

- Lead and mentor cross-functional teams, including data engineers, analysts, and data scientists, to deliver scalable and high-quality data solutions.

- Provide technical expertise and guidance on best practices in data architecture, cloud technologies, and data management principles.

Technologies and Expertise:

- Azure Synapse Analytics: Experience in orchestrating, designing, and running PySpark jobs for complex ETL and near real-time data pipelines.

- Azure Data Lake with Delta Lake: Expertise in implementing and managing Delta Lake for scalable and reliable data storage and versioning.

- SQL and NoSQL Databases: Proficiency in designing and optimizing relational and non-relational database systems to support large-scale data operations.

- PySpark: Strong experience in distributed data processing using PySpark for data transformation and enrichment.

- Parquet: Familiarity with Parquet format for efficient data storage and retrieval.

- Adobe Experience Platform (AEP): Experience with AEP or similar customer data platforms is a plus.

- CI/CD Pipelines: Hands-on experience in building and automating CI/CD pipelines to deploy and manage ETL processes.

Requirements:

- Bachelor’s degree in Computer Science, Information Technology, or a related field.

- Proven experience (5+ years) as a Data Architect or in a similar role, working with cloud-native data solutions.

- Fluent in English with excellent communication skills (written and verbal), capable of explaining complex technical concepts to non-technical stakeholders.

- Strong leadership skills with the ability to collaborate across teams and guide them in delivering high-quality data solutions.

- Analytical mindset with a passion for problem-solving and continuous improvement.

- Proven expertise in designing, implementing, and optimizing data architectures on Azure cloud platforms.

- Experience in data governance and compliance, ensuring adherence to data privacy and security standards.

Nice to Have:

- Azure certifications, such as Azure Data Engineer Associate or Azure Solutions Architect Expert.

- Familiarity with Adobe Experience Platform (AEP) or similar customer data platforms.

- Experience working with global, cross-border teams and international projects.

- Knowledge of FinOps principles to optimize data costs and cloud resource usage.

- Experience with data quality tools and frameworks for monitoring and improving data reliability.