
Data Engineering Leadership Role
4 days ago
We are seeking an experienced Senior Data Engineer to lead the development of a scalable data ingestion framework that efficiently extracts, transforms, and loads data from diverse sources.
Key Responsibilities:- Data Ingestion Framework: Architect, develop, and maintain an end-to-end data ingestion framework that efficiently extracts, transforms, and loads data from diverse sources.
- Framework Optimization: Utilize AWS services such as AWS Glue, Lambda, EMR, ECS, EC2, and Step Functions to build highly scalable, resilient, and automated data pipelines.
- Validation Processes: Develop and implement automated data quality checks, validation routines, and error-handling mechanisms to ensure the accuracy and integrity of incoming data.
- Monitoring & Reporting: Establish comprehensive monitoring, logging, and alerting systems to proactively identify and resolve data quality issues.
- Design & Implementation: Architect and develop secure, high-performance APIs to enable seamless integration of data services with external applications and internal systems.
- Documentation & Best Practices: Create thorough API documentation and establish standards for API security, versioning, and performance optimization.
- Professional Background: At least 5 years of relevant experience in data engineering with a strong emphasis on analytical platform development.
- Programming Skills: Proficiency in Python and/or PySpark, SQL for developing ETL processes and handling large-scale data manipulation.
- AWS Expertise: Extensive experience using AWS services including AWS Glue, Lambda, Step Functions, and S3 to build and manage data ingestion frameworks.
- Data Platforms: Familiarity with big data systems (e.g., AWS EMR, Apache Spark, Apache Iceberg) and databases like DynamoDB, Aurora, Postgres, or Redshift.
- API Development: Proven experience in designing and implementing RESTful APIs and integrating them with external and internal systems.
- CI/CD & Agile: Hands-on experience with CI/CD pipelines (preferably with GitLab) and Agile development methodologies.
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