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 Engineer) 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:
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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.
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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.
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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.
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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.
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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.
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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.
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Proactive Problem Solving: Go beyond fulfilling requirements — proactively identify improvement opportunities, anticipate internal customer needs, propose innovative solutions, and challenge approaches when necessary.
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Agile & High-Quality Delivery: Deliver reliable data solutions with speed and precision, balancing the need for rapid iteration with the highest quality standards.
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Stakeholder Communication: Articulate technical concepts clearly to non-technical audiences; facilitate alignments, present solution proposals, and document modeling and architecture decisions.
Requirements:
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Proven experience in a Data Engineering or Analytics Engineering role at senior level
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Advanced proficiency in SQL and Python for data analysis, transformation, and validation
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Solid experience in data modeling and data warehouse design
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Hands-on experience working with BigQuery (or other cloud-based data warehouse platform)
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Demonstrated ability to understand complex business logic and validate results from a business perspective, not just technical
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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
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Experience specifying and preparing data requirements for AI or machine learning applications
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Advanced/fluent English (reading, writing, and conversation) — constant communication with international stakeholders is required
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Track record of delivering high-impact projects quickly and with quality
Nice to Have:
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Experience with dbt (data build tool) for transformation and orchestration of analytical pipelines
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Familiarity with data ingestion tools such as Airbyte or Airflow
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Knowledge of automated testing best practices and CI/CD for data pipelines
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Prior experience in Supply Chain, Retail, or Consumer Goods industries
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Experience in projects involving data preparation for consumption via generative AI or conversational analytics platforms
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Experience building business-oriented analytical reports (web reporting, custom visualizations)
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