Design, develop, and maintain scalable data pipelines for efficient data ingestion and transformation
Optimize Airflow workflows and automate ETL processes to improve reliability, efficiency, and performance
Develop and maintain database schemas, ensuring efficient storage, retrieval, and query optimization
Implement data governance best practices, ensuring data quality, validation, and integrity
Build API integrations to ingest external data sources and provide structured access for analytics teams
Monitor, troubleshoot, and improve data infrastructure for scalability and fault tolerance
Collaborate with cross-functional teams to align data solutions with business needs
What You’ll Bring
Degree in Computer Science, Data Engineering, Information System, and evidence of exceptional ability or equivalent experience
3-5 years of work experience in Data Engineering, designing, and optimizing ETL pipelines in large-scale environments
Proficiency in Python and SQL for data processing, automation, and query optimization in production systems
Hands-on experience with workflow orchestration tools (e.g., Airflow) to automate and scale data pipelines
Strong understanding of database performance tuning in PostgreSQL or other relational databases, including indexing, partitioning, normalization, and query optimization
Experience working with containerized infrastructure using Kubernetes and implementing CI/CD automation with tools like Jenkins and GitHub Actions
Familiarity with API integrations for data ingestion and structured data serving, ensuring scalability and security