Senior Data Developer, Colombia

We are tech transformation specialists, uniting human expertise with AI to create scalable tech solutions.

With over 8,000 CI&Ters around the world, we’ve built partnerships with more than 1,000 clients during our 30 years of history. Artificial Intelligence is our reality.

CI&T is expanding its data development capabilities to support a greenfield platform initiative for a leading client in the agribusiness industry
. This new product is being built from the ground up to deliver AI-powered agronomic analysis with georeferenced map visualizations — and the quality of its data foundation will determine everything that follows.

This role sits at the core of that foundation. As a Senior Data Developer
, you will work alongside the client's technical leadership to architect and build the data ecosystem that will power intelligent agronomic insights. Your work will directly enable AI applications and geospatial visualizations to function on reliable, well-structured data — making this position both technically demanding and strategically critical. If you thrive in ambiguous, high-ownership environments where you shape the data architecture rather than inherit it, this is your role.

• Design and build end-to-end data pipelines across the RAW, Silver, and Gold layers of the Medallion Architecture, ensuring reliability, performance, and maintainability at each stage

• Architect data ingestion, transformation, standardization, and serving processes
  , structuring data flows from diverse and heterogeneous sources into a coherent analytical foundation

• Model data for analytical consumption following Data Warehouse best practices, including Star Schema design and dimensional modeling suited for business intelligence and AI-readiness

• Identify, evaluate, and consolidate new data sources relevant to agronomic business objectives, proactively engaging stakeholders to understand, obtain, and validate data availability and quality

• Interact with business stakeholders and client leadership to translate domain requirements into data architecture decisions, challenging assumptions and proposing solutions grounded in technical evidence

• Manipulate, optimize, and serve data in multiple formats — including Parquet, CSV, and geospatial datasets — tailored to the consumption needs of downstream AI applications and map-based visualizations

• Manage and configure cloud infrastructure end-to-end, including storage, compute, access control, serverless functions, data cataloging, and event-driven processing on AWS

• Own deployment and CI/CD practices for data pipelines — including repository management, branching strategy, test gates, and automated deploy workflows via Git Lab

• Support the creation of the data layer that will feed AI/ML applications
  , ensuring data quality, structure, and availability meet the requirements of machine learning workflows — without directly developing the models themselves

• Operate as a proactive technical partner in a greenfield environment: question, propose, experiment, and iterate with the team rather than execute in isolation

• English proficiency at B2 level or above — ability to explain technical flows, engage in discussions, ask clarifying questions, and collaborate effectively with international stakeholders (accent is not a barrier; communication clarity is)

• Solid hands-on experience with AWS
  , covering the full infrastructure spectrum: S3, IAM (permissions and security configuration), Redshift, Lambda (serverless use cases), and Glue (including Glue Catalog for metadata management); ability to evaluate trade-offs between services for different pipeline scenarios

• Experience with Terraform or equivalent Infrastructure-as-Code (IaC) tooling
  , applied recurrently in real data engineering projects — not just theoretical knowledge

• Proficiency with Git Lab for source control, CI/CD pipeline configuration, deployment workflows, and test gate management — specifically Git Lab, not just generic Git experience

• Strong proficiency in SQL
  , including complex query writing, analytical transformations, and performance tuning for data warehouse environments

• Strong proficiency in Py Spark , applied to…