Lead Data Engineer

Role Overview:

We are seeking a highly experienced Database and Data Engineer with strong expertise in designing, implementing, and managing large-scale data solutions. The ideal candidate will have deep knowledge of AWS services, database technologies, and data engineering frameworks, ensuring robust, scalable, and secure data pipelines and platforms.

Key Responsibilities:

• Design and implement data pipelines and ETL workflows using Apache NiFi, Spark, and other modern frameworks.
• Manage and optimize AWS RDS instances for Oracle and PostgreSQL, including performance tuning, backup, and recovery strategies.
• Architect and maintain data lakes on AWS S3 with Apache Iceberg for efficient data storage and querying.
• Develop and manage real-time data streaming solutions using AWS Kinesis.
• Implement Oracle GoldenGate (OGG) for data replication and synchronization across heterogeneous environments.
• Ensure data security, compliance, and governance across all platforms.
• Collaborate with cross-functional teams to define data requirements and deliver high-quality solutions.
• Troubleshoot and resolve complex data-related issues in production environments.

Required Skills & Expertise:

• AWS Services: RDS (Oracle & PostgreSQL), S3, Kinesis.
• Data Lake & Table Formats: Apache Iceberg.
• Data Integration & Streaming: Apache NiFi, Oracle GoldenGate (OGG).
• Big Data Processing: Apache Spark.
• Strong knowledge of SQL, PL/SQL, and database performance tuning.
• Experience with data modeling, data governance, and security best practices.
• Familiarity with CI/CD pipelines for data workflows.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Information Technology, or related field.
• 12+ years of experience in database administration, data engineering, and cloud-based data solutions.
• Proven track record of delivering large-scale, high-performance data platforms.

Nice-to-Have:

• Experience with AWS Glue, Athena, or similar query engines.
• Knowledge of containerization (Docker, Kubernetes) for data services.
• Exposure to data cataloging and metadata management tools.