[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.
Requirements
Solid experience in data development, with proven hands-on production experience on the Databricks platform
Strong proficiency in PySpark (DataFrame API, Spark SQL, UDFs, window functions) and Databricks SQL (ANSI SQL, MERGE INTO, COPY INTO, CTEs), including performance tuning such as partition pruning, file compaction, skew handling, and query optimization
Solid, practical experience with Delta Lake: MERGE/upsert patterns, ACID transactions, time travel, and table maintenance (OPTIMIZE, VACUUM, ZORDER, liquid clustering, Change Data Feed)
Demonstrated experience implementing Slowly Changing Dimensions (Type 1 and Type 2) and dimensional modeling concepts (star schema, fact/dimension design) — not requiring you to have designed a model from scratch, but requiring the mindset to understand and extend one
Experience with medallion (or comparable layered) architecture in a production data platform, and with Unity Catalog, jobs/workflows, secrets management, and notebook-based development
Experience with Git and Azure DevOps (or equivalent) for version control, pull requests, and CI/CD pipelines, along with Microsoft Azure services (Key Vault, Service Principal/Managed Identity, Data Lake Storage)
Ability to read and navigate a large, established codebase (400+ notebooks), learning and following existing conventions rather than rewriting them, and to ramp up quickly in a business-rule-heavy environment
Advanced English (C1 or above) communication skills, with the ability to work directly with US-based client stakeholders, propose technical recommendations, and align with decisions made by client-side technical leadership
Nice to Have
Experience with Databricks Asset Bundles or other Infrastructure-as-Code approaches for managing jobs, clusters, and permissions as code
Familiarity with Delta Live Tables and with Databricks Genie (AI/BI Genie) for natural-language querying and conversational analytics
Familiarity with data quality frameworks (e.g., Great Expectations, Soda Core, or custom validation frameworks)
Experience with pytest and databricks-connect for automated testing of Spark pipelines outside of manual notebook execution
Familiarity with Pydantic or similar typed-configuration approaches, and experience with schema migration/versioning approaches (e.g., Flyway, Liquibase, or custom frameworks)
Comfortable using AI-assisted development tools (e.g., GitHub Copilot, Cursor, or similar) to accelerate coding, debugging, and code review workflows
 
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