Summary
Rihal is seeking an Associate Data Engineer to design, build, and maintain scalable data pipelines and ETL processes. The role includes data validation and transformation, pipeline monitoring and troubleshooting, performance improvements, and documentation. Candidates should have a bachelor’s degree in a relevant field and at least three years of relevant experience, with knowledge of data warehouses, data lakes, data modeling, governance, and security. Experience with orchestration tools, SQL, Python, processing frameworks, cloud data platforms, Git, and basic CI/CD is requested.
The posting
As published on rihal.om. Pulled from the employer's site and shown as they wrote it.
Job Purpose:
The Associate Data Engineer will be responsible for designing, implementing, and maintaining scalable data pipelines and solutions to support our organization's data needs.
Duties & Responsibilities:
- Design and develop data pipelines and ETL processes to collect, process, and store data from various sources.
- Implement data quality checks, data validation, and data transformations to ensure the accuracy and integrity of data.
- Collaborate with data scientists and analysts to understand data requirements and design optimized data storage structures
- Optimize and tune data pipelines for performance and scalability.
- Monitor, troubleshoot, and debug data pipeline issues to ensure continuous data flow and availability.
- Stay informed about emerging technologies and best practices in data engineering and make recommendations for improvements to existing processes.
- Document data engineering processes, data models, and data flows for internal and external stakeholders.
Qualifications and Experience:
- Bachelor’s degree in computer science, Information Technology, or related field
- +3 years of experience working as a Data Engineer or similar role
- Experience with data pipeline orchestration tools (Airflow, Prefect, Dagster) and implementing logging or monitoring for reliability.
- A track record of working with data warehouses (star/snowflake schemas), data lakes, or lakehouse environments.
- Strong proficiency in SQL, Python, and data processing frameworks such as Apache Spark or Apache Kafka, Apache Flink and dbt.
- Experience with cloud-based data platforms (e.g., AWS, GCP, Azure) and data warehousing technologies
- Knowledge of data modeling concepts and database design principles
- Familiarity with data governance, data security, and data privacy regulations
- Familiarity with version control practices (Git) and basic CI/CD concepts for deploying data pipelines.
- Understanding NoSQL concepts or cloud object stores is a plus
- Excellent problem-solving skills and attention to detail
- Strong communication and collaboration skills