Education Requirement: Bachelor's Degree
Key Responsibilities:
- Design, develop, and maintain scalable ETL pipelines for ingesting, transforming, and loading structured and unstructured datasets.
- Analyze complex data structures and source-to-target mappings to identify opportunities for workflow optimization and automation.
- Monitor and ensure data accuracy, consistency, and integrity across systems and platforms.
- Implement data migration strategies to support application modernization and transitions across platforms or cloud environments.
- Collaborate with analysts, data scientists, developers, and business partners to translate requirements into effective data engineering solutions.
- Monitor pipeline performance, troubleshoot operational issues, and enhance reliability, scalability, and efficiency.
- Document data flows, transformation logic, and operational procedures for maintainability and knowledge sharing.
Required SkillsExperience
- 2+ Years Professional Experience
- Hands-on experience with data analysis, ETL development, and data migration.
- Proficiency in SQL, including writing complex queries, transformations, and performance tuning.
- Familiarity with Python for data manipulation, scripting, and workflow automation.
- Experience with ETL frameworks or orchestration tools such as Apache Airflow, Talend, dbt, or similar.
- Understanding of data warehousing principles including dimensional modeling and staging architecture.
- Exposure to cloud-based data platforms such as AWS Redshift, Google BigQuery, Azure SQL, or similar environments.
Preferred Qualifications
- Experience with cloud ecosystems such as AWS, Azure, or Google Cloud Platform.
- Knowledge of big data technologies (Hadoop, Spark, or distributed processing frameworks).
- Exposure to data visualization tools (Power BI, Tableau, Looker) and version control systems (e.g., Git).
- Experience designing automated data workflows or integrating workflow orchestration tools.
Core SkillsCompetencies
- ETL pipeline developmentoptimization
- SQLPython for data processing
- Data modeling and profiling
- Understanding of data warehousing concepts
- Cross-functional collaboration
- Problem-solvingroot cause analysis
- Ability to document technical processes clearly
- Continuous improvement mindset
