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 highly motivated Senior Data Developer (Analytics) who serves as a bridge between business and technology — someone who not only masters the technical stack but also brings strategic vision, deep understanding of business logic, and a proactive approach to solution design.

This professional will own the complete lifecycle of data products — from ingestion and modeling through final delivery — ensuring that data is structured, validated, and ready for consumption in AI and analytics environments. We need someone who communicates confidently with business stakeholders (both technical and non-technical), deeply understands the requirements behind requests, and translates them into robust, scalable technical solutions.

Responsibilities:

Business Translation & Requirements Definition: Actively engage with business stakeholders to understand context, challenges, and underlying needs; translate complex requirements into clear, detailed technical specifications that enable effective use of AI and analytics platforms.

End-to-End Product Ownership: Take full responsibility for the data products you build — from ingestion through modeling, transformation, validation, to final delivery and ongoing monitoring.

Data Quality Assurance & Validation: Implement and maintain rigorous validation processes, automated testing, and quality checks; act as guardian of data accuracy and integrity, ensuring metrics and KPIs accurately reflect business reality.

Data Modeling & Transformation: Design and implement data transformation layers that pre-aggregate business information, apply consistent business rules, and deliver datasets ready for consumption in dashboards and AI applications.

AI Enablement & Data Structuring: Collaborate with AI teams to specify and prepare structured data, data dictionaries, and models that enable AI platforms and intelligent reporting capabilities.

Full-Stack Data Development: Build and maintain data ingestion pipelines from multiple sources, implement transformations in SQL and Python, and ensure performance and scalability of solutions.

Proactive Problem Solving: Go beyond fulfilling requirements — proactively identify improvement opportunities, anticipate internal customer needs, propose innovative solutions, and challenge approaches when necessary.

Agile & High-Quality Delivery: Deliver reliable data solutions with speed and precision, balancing the need for rapid iteration with the highest quality standards.

Stakeholder Communication: Articulate technical concepts clearly to non-technical audiences; facilitate alignments, present solution proposals, and document modeling and architecture decisions.

Requirements:

Proven experience in a Data Engineering or Analytics Engineering role at senior level

Advanced proficiency in SQL and Python for data analysis, transformation, and validation

Solid experience in data modeling and data warehouse design

Hands-on experience working with BigQuery (or other cloud-based data warehouse platform)

Demonstrated ability to understand complex business logic and validate results from a business perspective, not just technical

Strong sense of ownership and proactive mindset — a professional who proposes improvements, challenges requirements when necessary, and doesn't wait to be asked to act

Experience specifying and preparing data requirements for AI or machine learning applications

Advanced/fluent English (reading, writing, and conversation) — constant communication with international stakeholders is required

Track record of delivering high-impact projects quickly and with quality

Nice to Have:

Experience with dbt (data build tool) for transformation and orchestration of analytical pipelines

Familiarity with data ingestion tools such as Airbyte or Airflow

Knowledge of automated testing best practices and CI/CD for data pipelines

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