FlatGigs is seeking a skilled
Data Engineer (AI and Python)
to join our dynamic team. In this role, you will build and maintain robust data pipelines and infrastructure that power AI-driven applications and analytics. You will work closely with data scientists and AI engineers to support scalable machine learning workflows and drive data innovation.
Key Responsibilities
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Design, develop, and maintain scalable ETL pipelines using Python to handle large volumes of structured and unstructured data
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Collaborate with AI teams to develop data solutions that enable efficient model training, evaluation, and deployment
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Implement data ingestion, cleansing, and transformation processes to ensure high-quality datasets for AI workflows
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Optimize data workflows and storage solutions for performance and reliability
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Work with cloud platforms (AWS, Azure, or GCP) to deploy and monitor data pipelines and related infrastructure
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Maintain documentation and enforce data governance and compliance policies
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Participate in code reviews, testing, and continuous integration to deliver robust production-ready data solutions
Requirements
Required Qualifications
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Bachelor's degree in Computer Science, Engineering, Data Science, or related field
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3+ years of professional experience in data engineering with a focus on AI and ML workflows
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Strong proficiency in Python programming and experience using libraries such as Pandas, NumPy, and Airflow
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Hands-on experience building and managing ETL/ELT pipelines
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Good understanding of cloud platforms (AWS, Azure, or GCP) and familiarity with deploying data infrastructure
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Experience with SQL and NoSQL databases
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Familiarity with container orchestration tools such as Docker and Kubernetes is a plus
Preferred Skills
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Knowledge of AI/ML concepts and ability to collaborate effectively with data science teams
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Experience with distributed data processing frameworks like Apache Spark
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Understanding of data security, compliance, and best practices in data engineering
Benefits
Market Competitive Salary
Leaves
Health Insurance
Hybrid Work Model