Roles and Skills
ECLARO delivers top talent for a variety of Machine Learning Data Engineering roles, tailored to support machine learning lifecycle needs in sectors such as finance, healthcare, tech, and retail. Although the specifics and titles may vary for each organization, our experienced recruiters have a track record of success in helping organizations hire at all levels
Junior Machine Learning Data Engineers
Assist in data preparation, cleaning, and ingestion, support ETL tasks, and maintain ML pipeline documentation
Mid-Level Machine Learning Data Engineers
Design and build data pipelines, integrate data from multiple sources, support model training workflows, and monitor data quality
Senior Machine Learning Data Engineers
Lead data engineering architecture, implement CI/CD for ML models, manage data lake/warehouse environments, and ensure production scalability and performance
Key skills
- Python and advanced programming in Java, Scala, or Go
- Machine learning frameworks and libraries (TensorFlow, PyTorch, scikit-learn)
- Data pipeline design and ETL development using tools like Spark, Kafka, or Airflow
- SQL and database design for large-scale data systems (relational and NoSQL)
- Cloud platforms (AWS, GCP, or Azure) with data engineering services
- Statistics and experimental design for model validation and A/B testing
- Communication skills to translate technical concepts for non-technical stakeholders
Frequently asked questions
What does a Machine Learning Data Engineer do on a day-to-day basis?
A Machine Learning Data Engineer bridges data science and engineering by designing and maintaining the data pipelines that feed machine learning models. Day-to-day work includes writing production-grade code to extract and transform data, optimizing data infrastructure for scale, collaborating with data scientists to ensure models have clean training data, debugging data quality issues, and monitoring system performance. They spend time coding, running tests, documenting systems, and troubleshooting issues that arise in production environments.
What are the key responsibilities of this role?
Key responsibilities include building and maintaining scalable data pipelines that serve machine learning workflows, designing schemas and data architectures to support model training and inference, ensuring data quality and reliability, optimizing query performance and system efficiency, collaborating with ML engineers and data scientists on feature engineering, implementing monitoring and logging for data systems, and documenting code and processes. The role requires balancing rapid experimentation with production stability.
What qualifications and experience are typical for this role?
Successful candidates typically have 3-7+ years of software engineering or data engineering experience, strong proficiency in Python and at least one other programming language, hands-on experience with big data tools and cloud platforms, solid understanding of machine learning concepts and model lifecycles, and experience with SQL and modern databases. Many candidates hold degrees in computer science, mathematics, engineering, or related fields, though practical experience is equally valued. Familiarity with data versioning, model monitoring, and MLOps practices is increasingly important.
How can we hire a Machine Learning Data Engineer through ECLARO?
ECLARO matches your specific technical requirements with qualified Machine Learning Data Engineers available for contract or full-time placement. Simply share your project scope, stack, team dynamics, and timeline with our recruiting team. We handle candidate sourcing, screening, and technical vetting so you can focus on evaluating fit. Whether you need someone for a short-term project, ongoing support, or permanent hire, ECLARO manages the staffing process to get you the right technical talent quickly.
Ways to hire through ECLARO
ECLARO can fill this role through contract or contract-to-hire staffing, direct placement, an Employer of Record (EOR), recruitment process outsourcing (RPO), or a dedicated offshore team in the Philippines (ECAPTIVE).