Data Engineer
Skills
Computer ScienceWarehouse OperationsApache SparkData AnalyticsData WarehousingDatabasesOptimization
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related field
- 5+ years of professional experience in Data Engineering
- Strong experience with Python and SQL
- Experience building and maintaining ETL and ELT pipelines
- Hands-on experience with cloud platforms such as AWS Azure or Google Cloud Platform
- Strong knowledge of data warehousing concepts and modern data architectures
- Experience working with large-scale datasets and distributed processing frameworks
- Strong understanding of database technologies including SQL and NoSQL databases
- Experience with workflow orchestration tools such as Airflow Prefect or similar platforms
- Knowledge of version control systems such as Git
- Excellent problem-solving and analytical skills
- Hands-on experience supporting AI and Machine Learning projects
- Experience working with AI-powered tools and platforms such as OpenAI Claude Gemini LangChain LlamaIndex Hugging Face vector databases
- Experience building data pipelines for AI model training and inference workflows
- Understanding of Retrieval-Augmented Generation architectures
- Experience preparing transforming and managing datasets for LLM-based applications
- Familiarity with AI model monitoring evaluation and performance optimization
- Experience integrating AI services and APIs into enterprise applications
Nice to have
- Experienced
- Analytical
- Collaborative
- Detail-oriented
- Adaptable
Day to day
- Design, develop, and maintain scalable ETL and ELT pipelines for structured and unstructured data.
- Build and optimize data warehouses, data lakes, and cloud-based data platforms that power analytics and AI initiatives.
- Collaborate with Data Scientists, AI Engineers, and stakeholders to deliver reliable datasets and support machine learning workflows.
- Create ingestion frameworks, enforce data quality and governance, troubleshoot production issues, and improve pipeline performance.