Job details
About this role
At CI&T, we help large enterprises transform the potential of AI into real business impact with AI Deployment, AI-native execution, and tech-integrated business solutions.
With 30 years of experience in technological transformation, we accelerate innovation with expertise in Agentic SDLC, Application modernization, Data & AI, Martech and Business strategy.
We are 8,000 CI&Ters across more than 25 countries, collaborating to build solutions with real impact. AI is already part of how we work, evolve, and innovate every day.
As CI&T continues to grow, we are looking for a skilled and self-driven Senior Data Developer to join a high-visibility initiative for a leading client in the healthcare and consumer health industry. This mobile field application supports field operations across Latin America, with the majority of its functionality built around data visualization powered by a centralized data lake. The product is now expanding into the United Kingdom, creating a critical need for someone who can extend and evolve the underlying data pipelines with precision and reliability.
This role covers the full lifecycle of data pipeline development and maintenance, from designing new ingestion flows to troubleshooting production issues. The ideal professional operates with strong technical autonomy while maintaining a sharp, questioning eye on data quality, format, and accuracy — contributing both hands-on execution and critical judgment to a mature, cloud-based data environment.
Responsibilities:
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Pipeline Development: Design, build, and maintain data pipelines for efficient ingestion into the data lake, using cloud-based services for orchestration, storage, and serverless processing.
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Market Expansion Support: Extend existing ETL jobs to incorporate new data sources as the product expands into new regions, ensuring no disruption to existing production workflows.
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Production Support: Monitor system performance and investigate production issues by tracing data flow through pipelines, identifying root causes, and resolving them to protect data integrity and availability.
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Data Transformation: Apply strong Python and distributed data processing skills to build scalable, efficient data solutions.
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Data Warehousing: Manage and maintain the data warehousing environment, ensuring performance and scalability as data volume grows.
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Data Manipulation: Use SQL to transform and analyze data, making information readily available in formats useful for business decision-making.
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Data Quality Management: Implement data quality management processes and apply data profiling practices to ensure integrity, accuracy, and quality across the full data lifecycle, from ingestion through visualization.
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Cross-functional Collaboration: Work closely with software development and solutions architecture teams by aligning on data structure and integration needs, ensuring data is accessible and properly modeled for consumption in the field-facing mobile application.
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Architecture Contribution: Contribute to the definition, evolution, and documentation of the data architecture, promoting best practices for data storage (lakes and warehouses) and processing (batch and real-time).
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Technical Documentation: Create and maintain clear, detailed documentation of ETL processes, data flows, and pipelines using collaborative documentation tools to support knowledge sharing across teams.
Requirements:
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Advanced or fluent English (C1 or above), required for daily collaboration with global stakeholders
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Hands-on experience designing and maintaining cloud-based data pipelines for data lake ingestion, using managed orchestration, storage, and serverless compute services
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Strong command of Python and a distributed data processing framework for building scalable data solutions
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Strong SQL skills for data transformation and analysis
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Experience managing and interacting with databases through query and administration tools
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Demonstrated ability to work with a high degree of technical autonomy while applying critical judgment on the quality, format, and accuracy of incoming data
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Full comfort working within an established cloud-based technology stack, with no resistance to cloud-native ways of working
Nice to Have:
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Experience applying generative AI tools and AI-powered coding assistants to speed up development and improve code quality, integrating with DevOps and test automation practices
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Experience supporting data needs for mobile applications
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Background in the healthcare, life sciences, or consumer health industry
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Experience supporting multi-region product expansions (e.g., LatAm to EMEA)
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Familiarity with data profiling and data quality management tooling
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