[Job - 30813] Senior Data Developer (Databricks), Colombia

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. We are seeking a Senior Data Developer (Databricks) to join our team and take ownership of a mission-critical data platform supporting business reporting and analytics for our client. This is an opportunity to work deep in the Databricks ecosystem — from raw ingestion through a fully modeled dimensional layer — while acting as the technical backbone the rest of the team relies on for platform expertise. This role blends hands-on execution with technical leadership: you will own and evolve notebooks across the full medallion architecture, design and extend dimensional modeling artifacts that power downstream business intelligence, and serve as the primary reviewer and technical reference point for the team. You'll operate both strategically — proposing architectural and process improvements — and operationally — troubleshooting production jobs, deploying across environments, and keeping documentation current. Success in this role requires the ability to ramp up quickly in a large, established, business-rule-heavy codebase and start contributing meaningfully within a short window. Responsibilities Delivery & Continuous Improvement: Work on assigned tickets and bugs while continuously looking for improvement opportunities beyond the immediate task — proactively identifying tech debt, refactoring opportunities, and automation gaps rather than limiting contributions to what's assigned. Data Pipeline Ownership: Own and evolve notebooks across the Stage, Bronze, Silver, and Gold layers, from ingestion through the dimensional model consumed by business intelligence reporting tools. Dimensional Modeling: Design and implement dimensional modeling artifacts — facts, dimensions, and slowly changing dimensions — with a clear understanding of how they enable downstream reporting. Workflow Management: Make changes to data processing jobs and workflows as needed to support evolving business requirements. Code Review & Quality: Review pull requests from other developers, enforcing code quality, performance, and architectural consistency across the codebase. Environment & Deployment Management: Deploy and promote changes across environments (Dev, QA, UAT, PROD), keeping deployment tracking up to date, and support environment operations such as restoring environments or tables from another environment or from a specific point in time. Production Monitoring & Troubleshooting: Monitor and troubleshoot daily production jobs, investigating failures and performance issues using platform-native diagnostic tools, job logs, and table history. Testing & Automation: Maintain and improve the automated testing pipeline, including CI/CD workflows and the underlying test framework. Documentation: Keep technical documentation current so institutional knowledge is not lost as the pipelines and connections the team relies on evolve. Technical Reference & Mentoring: Act as the go-to technical reference for the team on the data platform — the person others turn to when something needs deep platform expertise — and support other team members on data modeling and development topics. Stakeholder Collaboration: Propose and recommend architectural and process improvements, collaborating with the client's business and technical stakeholders — including the client's data architecture function — to translate requirements into scalable, well-tested data pipelines, while remaining equally comfortable taking direction from client-side technical leadership…