• Design, build, and maintain scalable, high-quality data pipelines for structured and unstructured data. • Implement robust data ingestion, transformation, and storage using cloud-based technologies. • Collaborate with stakeholders to understand business goals and translate them into data engineering solutions. • Monitor, troubleshoot, and optimize data pipelines for reliability and performance. • Support data validation, testing, and documentation processes. • Contribute to the design and deployment of modern data architectures (e.g., data lakes, lakehouses, data warehouses). • Apply Infrastructure-as-Code (IaC) practices for provisioning and managing cloud resources. • Integrate emerging tools and frameworks to modernize existing data environments. • Ensure security, governance, and compliance in all stages of data handling. • Work in agile teams, contributing to continuous improvement and mentoring junior team members.

• Define and lead end-to-end data architecture for complex ecosystems, balancing technical depth with business outcomes. • Translate business strategy into scalable technology solutions through discovery workshops, assessments, and roadmaps. • Act as technical sponsor for CI&T’s most strategic accounts, working closely with C-level clients and non-technical stakeholders. • Lead architectural design, technology selection, and proofs of concept for critical platforms and innovation programs. • Govern technical quality across delivery squads, driving adherence to security, performance, scalability, and privacy standards. • Support pre-sales, proposal development, RFPs, and technical visioning with account teams and clients. • Coach and mentor senior architects and technical leads, shaping technical career development paths.

• Design, implement, and optimize data pipelines using both batch and streaming processing frameworks. • Architect and maintain data lakehouse solutions using Apache Iceberg and object storage such as S3. • Implement scalable Data Vault and Star Schema models. • Build and manage real-time ingestion pipelines with Kafka, Spark, or Flink. • Integrate and orchestrate workflows using tools like Airflow, dbt, NiFi, or Airbyte. • Enforce data governance, data quality, and access control policies. • Troubleshoot pipeline performance and reliability issues.

• Design, build, and maintain scalable data pipelines and ETL/ELT workflows • Help to own and improve our data warehouse architecture in Snowflake • Implement data modeling best practices to support analytics and reporting needs • Drive data quality initiatives, including validation, testing, and monitoring frameworks • Partner with product engineering teams to design data contracts and integrate data capture into applications • Work closely with Analytics and Business Intelligence teams to understand reporting needs and deliver reliable data products • Collaborate with Security and Compliance to implement data governance and privacy best practices • Partner with Product and Engineering leadership on data strategy and technical roadmaps • Collaborate with Clinicians and Clinical Researchers to ensure they have the data available to deliver on their projects and support patient care • Work with Analysts to decrease latency, improve testability and integrate with de-identification providers to meet our goals and regulatory needs • Mentor and coach more junior data engineers, fostering growth and technical development • Establish and document data engineering standards, patterns, and best practices • Champion a culture of data quality, automation, and continuous improvement