Databricks Engineer / Big Data Engineer (Remote - Latam) at Jobgether

, , Argentina -

Full Time

Start Date

Immediate

Expiry Date

18 Jan, 26

Salary

0.0

Posted On

20 Oct, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Databricks, Big Data, Data Engineering, Apache Spark, Python, Scala, SQL, ETL, ELT, Cloud Technologies, Data Validation, Machine Learning, Data Quality, Collaboration, Analytics, Delta Lake

Industry

Internet Marketplace Platforms

Description

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Databricks Engineer / Big Data Engineer in Latin America. We are seeking a proactive Databricks Engineer to design, build, and maintain scalable data pipelines and solutions in a cloud-native environment. This role offers the opportunity to work closely with data scientists, architects, and analysts to deliver reliable, high-quality data for analytics and machine learning. You will leverage Databricks, Apache Spark, and modern cloud technologies to transform raw data into actionable insights. The position provides exposure to cutting-edge data engineering practices while working in a collaborative, fully remote Latin American team focused on innovation, efficiency, and performance. Accountabilities: Develop and maintain ETL/ELT pipelines using Databricks and Apache Spark. Transform raw data into structured, analytics-ready formats to support reporting and machine learning. Optimize performance of Databricks notebooks, workflows, and pipelines for efficiency and scalability. Integrate Databricks with cloud platforms (AWS, Azure, or GCP) and external data sources. Implement data validation, monitoring, and quality assurance processes to ensure reliable outputs. Collaborate with data scientists to support model training, deployment, and operationalization. Participate in architecture and code reviews, providing input to improve system reliability and maintainability. 2–5 years of experience in data engineering roles, ideally with Databricks and big data technologies. Strong proficiency in Apache Spark, Python, Scala, and SQL. Hands-on experience with Databricks notebooks, Delta Lake, and MLflow. Knowledge of cloud platforms such as Azure Databricks, AWS, or GCP. Understanding of data lakes, data warehouses, and big data concepts. Familiarity with data quality frameworks and monitoring best practices. English B2 or higher (conversational level). Strong analytical, problem-solving, and collaboration skills in remote, cross-functional teams. 100% remote work across Latin America. Competitive remuneration in USD. Exposure to modern data engineering tools and cloud technologies. Opportunities for professional growth and career development. Collaborative, flexible, and supportive remote work environment. Involvement in high-impact projects supporting analytics and AI initiatives. Jobgether is a Talent Matching Platform that partners with companies worldwide to efficiently connect top talent with the right opportunities through AI-driven job matching. When you apply, your profile goes through our AI-powered screening process designed to identify top talent efficiently and fairly. 🔍 Our AI thoroughly evaluates your CV and LinkedIn profile, analyzing your skills, experience, and achievements. 📊 It compares your profile to the job’s core requirements and past success factors to calculate a match score. 🎯 The top 3 candidates with the highest match are automatically shortlisted. 🧠 When necessary, our human team may perform additional review to ensure no strong candidate is overlooked. The process is transparent, skills-based, and unbiased, focusing solely on your fit for the role. Once the shortlist is completed, it is shared with the hiring company, who then determines next steps such as interviews or additional assessments. Thank you for your interest! #LI-CL1

Responsibilities

Develop and maintain ETL/ELT pipelines using Databricks and Apache Spark. Collaborate with data scientists to support model training, deployment, and operationalization.