Designing, developing, and maintaining data pipelines that ensure reliability, scalability, and discoverability.
Replicating existing data pipeline patterns to support new initiatives and optimize performance.
Writing reusable data utilities using common data-centric libraries and frameworks.
Building and maintaining ETL workflows (Airflow) and troubleshooting data pipeline issues.
Executing database operations manually and through Data Definition Language (DDL) scripts.
Writing optimized SQL queries and advising on appropriate database solutions.
Operating within AWS environments, managing and integrating cloud services effectively.
Developing infrastructure-as-code scripts for consistent, scalable data environments.
Implementing automated testing (unit, integration, end-to-end, contract) to maintain data reliability.
Collaborating with cross-functional teams to design and deliver solutions that meet both business and technical needs.

A degree in a technical field (e.g., Computer Science, Engineering) or equivalent professional experience.
At least 2 years of experience as a Data Engineer or in a similar technical role.
Strong understanding of data engineering principles, including database design patterns and ETL processes.
Proficiency in Python or PySpark, with the ability to write clean, efficient, and reusable code for data processing and transformation.
Hands-on experience with Apache Airflow for orchestrating and managing data pipelines.
Experience working with large, diverse datasets and building scalable data solutions.
Proficiency in SQL and familiarity with both SQL and NoSQL database technologies.
Experience with AWS cloud services, using both the console and CLI.
Knowledge of infrastructure as code (e.g., Terraform, AWS CloudFormation).
Familiarity with DevOps practices, version control, and CI/CD pipelines.
Strong analytical and problem-solving skills, with a keen eye for detail.
A collaborative mindset and enthusiasm for mentoring and cross-team engagement.

Data Engineer

Key skills