Additionalinformation

Additional Information

Our Perks and Benefits:

📚 Learning Opportunities:

• Certifications in AWS (we are AWS Partners), Databricks, and Snowflake.

• Access to AI learning paths to stay up to date with the latest technologies.

• Study plans, courses, and additional certifications tailored to your role.

• Access to Udemy Business, offering thousands of courses to boost your technical and soft skills.

• English lessons to support your professional communication.

🛫 Travel opportunities to attend industry conferences and meet clients.

👩 🏫 Mentoring and Development:

• Career development plans and mentorship programs to help shape your path.

🎁 Celebrations & Support:

• Special day rewards to celebrate birthdays, work anniversaries, and other personal milestones.

• Company-provided equipment.

⚖️ Flexible working options to help you strike the right balance.

Other benefits may vary according to your location in LATAM. For detailed information regarding the benefits applicable to your specific location, please consult with one of our recruiters.

Companydescription

Company Description

Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com

Jobdescription

Job Description

We are looking for a skilled Data Engineer with 4+ years of experience to design, build, and maintain scalable data pipelines and platforms using Azure and Databricks. The ideal candidate will work closely with analytics, data science, and business teams to deliver reliable, high-performance data solutions that enable data-driven decision-making.

The ideal candidate has strong experience working with code repositories, building data pipelines in Databricks, and designing scalable data models to support evolving analytics needs in cross-functional environments.

Responsibilities

• Design, develop, and optimize end-to-end data pipelines using Azure Databricks
• Refactor legacy code to simplify maintenance, updates, and reuse across multiple use cases.
• Design and build modular, reusable code components to support multiple journeys and reduce duplication.
• Consolidate key KPIs, metrics, and attributes into standardized data structures to enable flexible journey views.
• Build and maintain scalable data models to support current and future journey analytics use cases.
• Ensure data quality, performance, and reliability across data pipelines and analytics datasets.
• Collaborate with analytics and engineering teams to improve data processes and architecture.

Qualifications

• 4+ years of experience in Data Engineering.
• Solid experience with Databricks.
• Strong experience working with GitHub repositories and version control workflows.
• Hands-on experience developing and maintaining data pipelines in Azure Databricks.
• Strong experience with SQL and large-scale data processing.
• Proven experience refactoring and maintaining legacy codebases.
• Hands-on experience with Apache Spark (PySpark preferred).
• Experience working with DBT for data transformation and modeling.
• Strong understanding and hands-on experience with Medallion Architecture (Bronze, Silver, Gold).
• Strong understanding of data modeling and reusable component design.
• Experience building scalable data models for analytics and reporting use cases.
• Strong focus on data quality, performance, and reliability.
• Ability to work in cross-functional environments and contribute to continuous improvement.
• Ability to work independently and take ownership of initiatives after receiving high-level direction, driving tasks forward with minimal supervision.