Since 2017, CI&T and Databricks have collaborated to help enterprise customers gather valuable insights from their data. Together, we significantly influence our clients' Data, AI, and ML journeys. With over 200 seasoned Databricks practitioners and a track record of successful enterprise projects, the partnership between CI&T and Databricks is exceptionally robust.

We're growing fast and seeking a talented, motivated Databricks Senior Data Engineer to join our team. You'll play a crucial role in developing and maintaining scalable data pipelines and infrastructure to drive data analytics and machine learning solutions for our clients.

Key Responsibilities

Design and Develop Data Pipelines: Create robust, scalable data pipelines using Databricks, Apache Spark, and SQL to transform and process large datasets efficiently.

Data Architecture: Collaborate with data architects to design data models and architecture that support data analytics and machine learning applications.

Performance Optimization: Monitor and optimize the performance of existing data pipelines and workflows to ensure high throughput and low latency.

Collaboration with Stakeholders: Work closely with data scientists, analysts, and business stakeholders to understand data requirements and translate them into technical specifications.

Data Quality and Governance: Implement data quality checks and governance practices to ensure data consistency, accuracy, and compliance.

Documentation and Best Practices: Maintain comprehensive documentation for data pipelines and processes, and contribute to the establishment of best practices within the team.

Mentorship: Provide guidance and mentorship to junior team members, fostering a culture of learning and collaboration.

Requirements for this Challenge

Experience: 5+ years of experience in data engineering or a related field, with a focus on data pipelines and ETL processes.

Soft Skills: Excellent communication in English and Portuguese, with the ability to work effectively in a team-oriented environment and engage with clients.

Technical Skills: Proficiency in Databricks, Apache Spark, SQL, and Python (or Scala).

Cloud Technologies: Experience with cloud platforms such as Azure, AWS, or GCP, particularly related to data storage and processing solutions.

Big Data Technologies: Experience with additional big data technologies such as Hadoop, Kafka, or Airflow.

Data Modeling: Strong understanding of data modeling concepts and experience with relational and NoSQL databases.

Problem-Solving: Excellent analytical and problem-solving skills, with the ability to troubleshoot complex data issues.

Education: Bachelor’s degree in Computer Science, Information Technology, or a related field.

Nice to Have

Certifications: Databricks certification (e.g., Databricks Certified Developer) or other relevant certifications.

Machine Learning: Familiarity with machine learning frameworks and concepts, and experience integrating data engineering with machine learning workflows.

Data Visualization: Knowledge of data visualization tools (e.g., Tableau, Power BI) to help communicate insights effectively.

Agile Methodologies: Experience working in Agile development environments and familiarity with tools such as JIRA or Confluence.