The Platform architect leads and implements best-in-class data management strategies and practices in standing up and managing the enterprise data platform (Data Lakehouse). They will be responsible for designing and building new capabilities to integrate into the platform – from researching new technologies/services to building POCs, training and mentoring others. Additionally they will collaborate with the data product teams to ensure the platform enables value.
Requirements
- Extensive hands-on experience with AWS services related to data storage and compute (e.g., S3, Glue, EMR, Redshift, Athena, Lambda).
- Proven experience in designing and implementing Data Lakehouses or Data Lakes on AWS using Lake Formation
- Ability to design scalable, reliable, and secure architectures that meet business needs.
- Experience implementing cloud services with an infrastructure as code IaC methodology using AWS CloudFormation, Ansible, & Terraform
- Familiarity with Apache Iceberg or similar table formats for handling large-scale data in a Lakehouse environment.
- Experience in enabling monitoring using system observability tools like Splunk, Prometheus (with Grafana)
- Experience harvesting and leveraging metadata to drive technical processes and implementation.
Responsibilities
- Architect and Design the Data Lakehouse: Lead the design and implementation of a scalable and secure Data Lakehouse on AWS, including data storage and compute layers.
- Storage Solutions: Design and implement storage solutions using AWS services like S3, Iceberg,
- Integrate relevant metadata from platform with data catalog and/or metadata management solutions.
- Compute Resources: Architect and optimize compute resources using AWS services like Glue, EMR, and Lambda for ETL processes, and possibly Redshift or Athena for query execution.
- Develop POCs, POVs and pilots to test architecture, capabilities etc. and collaborate with collaborate with data engineers to ensure seamless integration and ingestion of data from various sources into the Lakehouse.
- Security and Compliance: Implement best practices for data security, including encryption, IAM roles, and compliance with relevant data protection regulations.
- Performance Optimization: Continuously monitor and optimize the performance of the data lakehouse, including storage costs and compute efficiency.
Other
- Collaboration: Work closely with data engineers, data scientists, and business stakeholders to ensure the platform meets their needs for data products.
- Documentation and Training: Provide thorough documentation and training to the internal team on the architecture and use of the Data Lakehouse.
- Strong analytical and problem-solving skills with attention to detail.
- Collaboration and Communication: Excellent communication skills and the ability to work effectively in a collaborative environment.
- Ability to mentor junior staff.
