We are looking for a Data Engineer with strong experience in building scalable data pipelines and exposure to Machine Learning workflows.
This role focuses on designing and maintaining robust data infrastructure while supporting data-driven and AI-powered applications. You will work closely with data scientists, engineers, and product teams to ensure data is reliable, accessible, and ready for advanced use cases.
We are looking for someone who combines strong engineering fundamentals with the ability to support ML pipelines and data workflows in production environments.
Key Responsibilities:
- Design, build, and maintain scalable data pipelines using Python and Airflow
- Develop and optimize ETL/ELT processes for structured and unstructured data
- Collaborate with data science teams to support Machine Learning workflows
- Ensure data quality, reliability, and performance across systems
- Work with large datasets and optimize queries and transformations
- Integrate data from multiple sources and external systems
- Monitor and improve pipeline performance and reliability
- Support deployment and maintenance of data-driven and ML-enabled applications
Must-Have:
- 4+ years of experience in Data Engineering or similar roles
- Strong proficiency in Python for data processing and pipeline development
- Hands-on experience with Apache Airflow (or similar orchestration tools)
- Experience building and maintaining ETL/ELT pipelines in production
- Strong knowledge of SQL and relational databases
- Experience working with large-scale datasets
- Exposure to Machine Learning workflows or data pipelines supporting ML models
- Experience working with cloud environments (AWS, GCP, or Azure)
- Strong problem-solving skills and ability to work independently
Nice to Have:
- Experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn)
- Experience with data warehouses (Snowflake, BigQuery, Redshift)
- Experience with streaming technologies (Kafka, Kinesis)
- Familiarity with feature engineering and model data preparation
- Experience with CI/CD pipelines for data workflows
Skills Required
- 4+ years of experience in Data Engineering or similar roles
- Strong proficiency in Python for data processing and pipeline development
- Hands-on experience with Apache Airflow or similar orchestration tools
- Experience building and maintaining ETL/ELT pipelines in production
- Strong knowledge of SQL and relational databases
- Experience working with large-scale datasets
- Exposure to Machine Learning workflows or data pipelines supporting ML models
- Experience working with cloud environments (AWS, GCP, or Azure)
- Strong problem-solving skills and ability to work independently
What We Do
We are on a mission to give every company, no matter the size, the opportunity to innovate and help build a better future. Our Services: Dedicated Tech (full/partial) Squads: we create multi-disciplinary, remote (near-shore) Tech Squads that become part of your team. They adapt to your workflows and are trained on Agile methodologies to deliver continuous value. We believe that well-trained remote teams bring clients the opportunity to increase innovation output by accessing a greater / more diverse pool of talent, while reducing the cost of development. On-Demand Software Development: at our core, we are software developers excited about building digital products and solutions using the latest technologies and agile methodologies. We provide end-to-end capabilities to deliver on your technical requests. Product Management, Tech Architecture, Front / Back End Development, Devops & QA Venture Building: we partner with companies to co-launch new digital businesses that leverage core assets of the company (distribution channels, customer base, industry knowledge, proprietary technology, etc). We take the co-created ideas into MLP’s (Most Lovable Product) aiming to find product market fit and scale in the leanest possible way. As startup founders ourselves, we love getting things from 0 to 1. We are End-To-End Innovation Enablers, helping your company unlock it's full innovation potential.
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