Data Engineer Lead

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

Job Description
We’re looking for a  Lead Data Engineer  with strong expertise in  Snowflake-based data platforms , modern data architectures, and AWS, to lead the design and implementation of scalable ingestion and validation solutions for a leading enterprise client.
This is a  client-facing leadership role , ideal for engineers who combine deep technical expertise with strong communication skills and a proactive mindset. You will play a key role in ensuring high-quality, governed, and reliable data pipelines that power critical business and analytics workflows.
What will you be doing?
- Lead the  design and implementation of data ingestion architectures in Snowflake , ensuring scalability and reliability.
- Own the development of  end-to-end ELT pipelines  integrating multiple data sources.

- Design and enforce  data quality frameworks , including validation rules, testing strategies, and monitoring.
- Apply and drive  Medallion Architecture (Bronze, Silver, Gold)  best practices across data pipelines.
- Build and optimize  data pipelines and architectures on AWS  (S3, Glue, Lambda, EMR, etc.).
- Develop and enhance  data validation and ingestion frameworks , including UI components (Streamlit-based) when needed.
- Act as a  technical leader , guiding best practices in data engineering, governance, and pipeline reliability.
- Proactively  identify risks, bottlenecks, and data quality issues , and implement mitigation strategies before they impact delivery.
- Contribute to  data governance initiatives , including data definitions, lineage, and stewardship practices.
- Drive documentation and continuous improvement of data platform processes.

What are we looking for?
- 5+ years of experience in Data Engineering  (strong senior-level or lead mindset expected).
- Strong hands-on experience with Snowflake (Must) .
- Strong experience building  ELT pipelines and data ingestion solutions .
- Solid experience with  SQL and large-scale data processing .
- Strong understanding and hands-on experience with  Medallion Architecture (Plus) .
- Experience building and maintaining  data pipelines in AWS (Must)
- Experience with Clean room (e.g., AMC) (Plus)
- Experience with  CI/CD pipelines for data workflows (Plus) .
- Experience with tools such as  dbt, Glue, Athena, EMR, Lambda, Terraform, or CloudFormation (Plus) .

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.