Analytics Engineer at Blend360

Buenos Aires, , Argentina -

Full Time

Start Date

Immediate

Expiry Date

17 Aug, 26

Salary

0.0

Posted On

19 May, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Dbt, SQL, Data Modeling, AWS Athena, AWS Redshift Serverless, Python, Dimensional Modeling, Layered Architecture, Incremental Models, Data Quality Testing

Industry

Professional Services

Description

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 We are seeking an Analytics Engineer to contribute to our next level of growth and expansion. Job Description What is this position about? Implement the curated data model layer in dbt across 18 data domains, working closely with the Onshore Analytics Engineering Lead. Design and build dbt models across the full layered architecture: staging, intermediate, and mart layers. Implement comprehensive dbt testing strategies, including unique, not_null, accepted_values, relationships, and custom tests to ensure data quality and reliability. Develop and optimize incremental models to handle high-volume domains processing billions of events efficiently. Generate and maintain thorough project documentation, including column descriptions and lineage graphs, to support transparency and team collaboration. Qualifications Advanced proficiency in dbt, including model development, testing frameworks, macros, and incremental strategies. Expert-level SQL skills, with strong ability to write performant, maintainable queries across large datasets. Solid understanding of data modeling principles, including dimensional modeling and layered architecture best practices. Hands-on experience with AWS data services, particularly Athena and Redshift Serverless. Working knowledge of Python for data engineering tasks and pipeline support. Ability to work cross-functionally across multiple data domains and communicate technical concepts clearly to stakeholders. What about languages? English: Advanced (required for effective communication with global teams). How much experience must I have? 3+ years of experience in Analytics Engineering or Data Modeling roles, with hands-on expertise building layered dbt pipelines and working with cloud-based analytical data platforms. 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. So what are the next steps? Our team is eager to learn about you! Send us your resume or LinkedIn profile below and we'll explore working together!

Responsibilities

Implement and design curated data model layers using dbt across multiple data domains, covering staging, intermediate, and mart layers. Develop incremental models for high-volume data and maintain comprehensive project documentation and lineage graphs.