Job
Description
Bachelor's degree in Computer Science, Information Systems, or a related field is desirable but not essential. Experience with data warehousing concepts and tools (e.g., Snowflake, Redshift) to support advanced analytics and reporting, aligning with the teams data presentation goals. Skills in working with APIs for data ingestion or connecting third-party systems, which could streamline data acquisition processes. Proficiency with tools like Prometheus, Grafana, or ELK Stack for real-time database monitoring and health checks beyond basic troubleshooting. Familiarity with continuous integration/continuous deployment (CI/CD) tools (e.g., Jenkins, GitHub Actions). Deeper expertise in cloud platforms (e.g., AWS Lambda, GCP Dataflow) for serverless data processing or orchestration. Knowledge of database development and administration concepts, especially with relational databases like PostgreSQL and MySQL. Knowledge of Python programming, including data manipulation, automation, and object-oriented programming (OOP), with experience in modules such as Pandas, SQLAlchemy, gspread, PyDrive, and PySpark. Knowledge of SQL and understanding of database design principles, normalization, and indexing. Knowledge of data migration, ETL (Extract, Transform, Load) processes, or integrating data from various sources. Knowledge of cloud-based databases, such as AWS RDS and Google BigQuery. Eagerness to develop import workflows and scripts to automate data import processes. Knowledge of data security best practices, including access controls, encryption, and compliance standards. Strong problem-solving and analytical skills with attention to detail. Creative and critical thinking. Strong willingness to learn and expand knowledge in data engineering. Familiarity with Agile development methodologies is a plus. Experience with version control systems, such as Git, for collaborative development Role & responsibilities Preferred candidate profile
