Senior Data Engineer at Blend360

Bogotá, Bogota D.C., Colombia -

Full Time

Start Date

Immediate

Expiry Date

18 Feb, 26

Salary

0.0

Posted On

20 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, SQL, Data Transformation, AWS, ETL, Data Cleaning, Financial Datasets, Machine Learning, Data Science, AI, Git, Analytical Skills, Problem-Solving, Debugging, Ad-Hoc Requests, Communication

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 a Senior Data Engineer to contribute to our next level of growth and expansion. If you're based in Argentina you can work 100% remotely. If you're located in Montevideo or Colombia you also have the option to work from our local office. Job Description Build, maintain, and optimize data pipelines in AWS for processing and integrating diverse data sources. Clean, transform, validate, and structure financial and analytical datasets for downstream consumption. Develop and enhance Python scripts and workflows, including data transformations using Pandas and SQL. Automate manual and repetitive workflows to increase operational efficiency. Integrate external systems and data providers such as Bloomberg (a strong plus). Support the development of ML/AI-ready datasets and pipelines (experience in ML/AI is a plus). Create and maintain schemas and tables within cloud-based environments. Handle ad-hoc data requests with agility and precision. Participate in cross-functional meetings and contribute to improving data processes and architecture. Qualifications Strong experience with Python, SQL, and data transformation using Pandas. Hands-on experience building and maintaining data pipelines in AWS. Experience with ETL/ELT processes and data cleaning. Familiarity with financial datasets or systems such as Bloomberg (a plus). Experience in financial services (a plus). Exposure to Machine Learning, Data Science, or AI workflows (a plus). Familiarity with Git. Strong analytical and problem-solving skills. Ability to work with large internal codebases, debug processes, and propose improvements. Ability to handle ad-hoc requests and operate under tight deadlines. What about languages? You will need excellent written and verbal English (B2 or more) for clear and effective communication with the team. 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.

Responsibilities

The Senior Data Engineer will build, maintain, and optimize data pipelines in AWS, processing and integrating diverse data sources. They will also clean, transform, validate, and structure financial and analytical datasets for downstream consumption.