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 looking for a Senior Data Developer with strong knowledge in developing and maintaining data pipelines in Databricks, Medallion Architecture, integrating and transforming data from various sources, such as APIs, relational databases, files, and many others!
As a senior member of the team, you will also provide technical guidance, mentor peers, and collaborate with cross-functional teams including data scientists, analysts, and platform engineers.
As a Senior Data Developer, you will lead the design and development of robust data pipelines, integrating and transforming data from diverse data sources such as APIs, relational databases, and files. Collaborating closely with business and analytics teams, you will ensure high-quality deliverables that meet the strategic needs of our organization.
Your expertise will be pivotal in maintaining the quality, reliability, security and governance of the ingested data, therefore driving our mission of Collaboration, Innovation, & Transformation.
Key Responsibilities
Develop and maintain data pipelines.
Integrate data from various sources (APIs, relational databases, files, etc.).
Collaborate with business and analytics teams to understand data requirements.
Ensure quality, reliability, security and governance of the ingested data.
Follow modern DataOps practices such as Code Versioning, Data Tests and CI/CD.
Document processes and best practices in data engineering.
Proven experience in building and managing large-scale data pipelines in Databricks (PySpark, Delta Lake, SQL).
Strong programming skills in Python and SQL for data processing and transformation.
Deep understanding of ETL/ELT frameworks, data warehousing, and distributed data processing.
Hands-on experience with modern DataOps practices: version control (Git), CI/CD pipelines, automated testing, infrastructure-as-code.
Familiarity with cloud platforms (AWS, Azure, or GCP) and related data services.
Strong problem-solving skills with the ability to troubleshoot performance, scalability, and reliability issues.
Proficiency in Git.
Advanced English is essential.
Experience with data contracts, schema evolution, and ensuring compatibility across services.
Expertise in data quality frameworks (e.g., Great Expectations, Soda, dbt tests, or custom-built solutions).
Integration with Power BI.
Skills Required
- Proven experience building and managing large-scale data pipelines in Databricks using PySpark, Delta Lake, and SQL
- Strong programming skills in Python and SQL for data processing and transformation
- Deep understanding of ETL/ELT frameworks, data warehousing, and distributed data processing
- Hands-on experience with DataOps practices, including Git, CI/CD pipelines, automated testing, and infrastructure-as-code
- Familiarity with AWS, Azure, or GCP and related data services
- Strong problem-solving skills for performance, scalability, and reliability issues
- Proficiency in Git
- Advanced English proficiency
- Experience with data contracts, schema evolution, and service compatibility
- Expertise with data quality frameworks such as Great Expectations, Soda, dbt tests, or custom solutions
- Experience integrating with Power BI
What We Do
CI&T is your end-to-end digital transformation partner. As a digital native, we bring a 27-year track record of accelerating business impact through complete and scalable digital solutions. With a global presence of 6,000+ professionals in strategy, research, data science, design and engineering, we unlock top-line growth, improve customer experience and drive operational efficiency.
