About the job
FlatGigs is on the lookout for a talented Data Engineer specializing in AI and Python to enhance our vibrant team. In this pivotal role, you will design and maintain scalable data pipelines and infrastructure that support AI-driven applications and analytics. Collaborating closely with data scientists and AI engineers, you will facilitate efficient machine learning workflows and foster data innovation.
Key Responsibilities
- Craft, develop, and sustain scalable ETL pipelines utilizing Python to manage extensive structured and unstructured data.
- Partner with AI teams to architect data solutions that streamline model training, evaluation, and deployment.
- Execute data ingestion, cleansing, and transformation processes to ensure exceptional dataset quality for AI workflows.
- Enhance data workflows and storage solutions for optimal performance and reliability.
- Leverage cloud platforms (AWS, Azure, or GCP) to deploy and oversee data pipelines and supporting infrastructure.
- Document processes and uphold data governance and compliance policies.
- Engage in code reviews, testing, and continuous integration to produce robust, production-ready data solutions.
Requirements
Required Qualifications
- Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field.
- A minimum of 3 years of professional experience in data engineering, focusing on AI and ML workflows.
- Expertise in Python programming with experience in libraries such as Pandas, NumPy, and Airflow.
- Practical experience in building and managing ETL/ELT pipelines.
- Solid understanding of cloud platforms (AWS, Azure, or GCP) and familiarity with deploying data infrastructure.
- Proficiency in SQL and NoSQL databases.
- Experience with container orchestration tools like Docker and Kubernetes is an advantage.
Preferred Skills
- Knowledge of AI/ML concepts and ability to collaborate effectively with data science teams.
- Experience with distributed data processing frameworks such as Apache Spark.
- Familiarity with data security, compliance, and best practices in data engineering.
Benefits
Competitive salary, leave entitlements, health insurance, and a hybrid work model.

