ETL Developer / Data Engineer

Key Responsibilities

• Design, develop, and maintain ETL processes and scalable data pipelines for enterprise data integration.
• Build data ingestion and transformation workflows using Java and/or Python.
• Develop and integrate REST APIs to support data exchange between applications and enterprise systems.
• Write, optimize, and maintain SQL queries for data extraction, transformation, validation, and performance tuning.
• Work with Oracle, Snowflake, and/or PostgreSQL databases to support data storage, processing, and integration requirements.
• Implement data quality checks, error handling, logging, and monitoring to ensure data accuracy and pipeline reliability.
• Troubleshoot data pipeline failures, integration issues, and database performance bottlenecks.
• Collaborate with data engineers, application developers, architects, and business stakeholders to deliver reliable data solutions.
• Follow established coding standards, version control practices, and deployment procedures.
• Support CI/CD processes and contribute to the continuous improvement of data engineering workflows.

Required Qualifications

• Strong programming experience in Java and/or Python.
• Hands-on experience developing and maintaining ETL processes and data pipelines in enterprise environments.
• Practical experience working with REST APIs for data ingestion and system integration.
• Strong SQL skills, including data transformation, complex queries, and performance tuning.
• Experience with Oracle and/or Snowflake/PostgreSQL databases.
• Understanding of data integration patterns, data processing workflows, and data quality principles.
• Strong analytical, problem-solving, and troubleshooting skills.
• Ability to collaborate effectively with technical teams in a fast-paced enterprise environment.

Preferred Experience

• Familiarity with CI/CD tools and practices, including Jenkins, Git, and Bitbucket.
• Knowledge of data ingestion frameworks and enterprise integration tools.
• Experience working with Snowflake-based data platforms and modern data pipeline architectures.
• Familiarity with automated deployment processes, version control, and software development best practices.
• Experience supporting cloud-based or enterprise-scale data engineering solutions.