1w ago
Senior AWS Data Engineer
Senior AWS Data Engineer
Requirements
- Degree in Engineering, Computer Science, or a related technical field.
- Proven experience building and maintaining data pipelines in AWS environments (S3, ECS, Lambda, RDS, SNS/SQS, IAM, CloudWatch).
- Strong expertise in SQL, including complex analytical queries, and experience with analytical and non-relational databases.
- Solid programming skills in Python and experience with data-related libraries such as Pandas and Boto3.
- Good understanding of data platform architecture, data governance, and best practices.
- Hands-on experience with version control systems such as GitHub or GitLab.
- Experience with Snowflake is a strong advantage.
- Familiarity with DBT Core is a plus.
- Experience with orchestration tools (e.g. AWS Step Functions, Airflow, Prefect).
- Exposure to CI/CD practices and Infrastructure as Code tools (Terraform, CloudFormation) is appreciated.
- Knowledge of data visualization tools (e.g. Tableau) is a plus.
- AWS and/or Snowflake certifications are considered an advantage.
Original Advert
- Design, implement, and operate scalable cloud-based data pipelines to ingest, process, and transform data coming from diverse sources such as APIs and secure file transfers.
- Build and maintain both batch and streaming data solutions with a strong focus on reliability, performance, and observability.
- Ensure data pipelines are monitored, documented, and supported to enable efficient usage by analytics and business teams.
- Structure and manage complex datasets while meeting business, technical, and performance requirements.
- Contribute to data governance initiatives by documenting datasets, maintaining metadata, and improving data quality standards.
- Develop and execute data validation, functional, and non-functional tests across the data platform.
- Design and maintain efficient data models and database objects, including tables, views, and transformation scripts.
- Analyze, troubleshoot, and optimize SQL queries to support high-performance analytics workloads.
- Participate in production support activities, incident resolution, and continuous monitoring.
- Collaborate with cross-functional teams to translate functional requirements into robust technical solutions.
- Apply architectural best practices and continuously improve the scalability, maintainability, and performance of data solutions.
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