Job Summary :

The Senior AWS Data Lake Architect is responsible for designing, building, and scaling the organizations AWS-based data lake platform, with a strong focus on enabling AI and machine learning solution deployment. This role owns the end-to-end architecture of the data lake ecosystem - from ingestion and storage through governance and consumption - and partners closely with data science and AI engineering teams to operationalize AI models in production. The role also provides technical leadership and mentorship across the data engineering function.

Responsibilities :

- Architect, design, and implement scalable, secure AWS data lake solutions (Amazon S3, AWS Lake Formation, AWS Glue, Amazon EMR, Amazon Redshift, Amazon Athena, Amazon Kinesis/MSK), including reference architecture, data zoning, partitioning strategy, and schema/catalog management (25%).

- Lead the design and deployment of AI/ML-ready data pipelines, including feature stores and model training pipelines, and integrate with Amazon SageMaker and Amazon Bedrock to support generative AI and machine learning use cases (20%).

- Partner with data science and AI engineering teams to operationalize AI solutions in production, including model deployment pipelines, MLOps integration, and inference data flows (15%).

- Establish and enforce data governance, security, and compliance standards, including IAM, KMS encryption, Lake Formation permissions, and audit logging (15%).

- Drive cost optimization and performance tuning across storage, compute, and query layers, and evaluate new AWS services and architectural patterns (15%).

- Provide technical leadership and mentorship to data engineers, own technical documentation and architecture decision records, and support operational handoffs for production issues (10%).

Minimum Qualifications:

- Bachelor's degree in Computer Science, Information Systems, Engineering, or related field required; Master's degree preferred.

- 8+ years' experience in data engineering or data architecture required; 5+ years specifically architecting solutions on AWS required.

- Hands-on experience designing and operating AWS data lake architectures, including Amazon S3, AWS Lake Formation, AWS Glue, Amazon EMR, Amazon Redshift, Amazon Athena, and Amazon Kinesis/MSK.

- Demonstrated experience deploying or supporting AI/ML solutions in production, including integration with Amazon SageMaker and Amazon Bedrock.

- Proficiency in Python and/or Scala/Spark for large-scale data processing.

- Strong understanding of data governance, security, and compliance best practices in cloud environments.

Desired Qualifications :

- AWS Certified Data Engineer - Associate (DEA-C01) or equivalent AWS certification (e.g., AWS Certified Solutions Architect - Professional).

- Experience with modern lakehouse formats such as Apache Iceberg, Delta Lake, or Apache Hudi.

- Experience architecting generative AI solutions on AWS using Amazon Bedrock, including foundation model access, agents, and knowledge bases.

- Exposure to infrastructure-as-code (Terraform, AWS CloudFormation, or AWS CDK) for provisioning data platform resources.

- Experience operating in regulated or large-scale enterprise environments with strict data governance requirements.