Data Architect

Job Description: Requirement/Must Have: 7+ years of experience in data architecture, data engineering, or related roles. Expert-level experience with AWS Glue, S3, Athena, Lambda, Step Functions, and IAM. Strong hands-on experience with ETL/ELT development and distributed processing. Proficiency in DAG-based orchestration (Airflow, MWAA, Step Functions, Glue Workflows). Experience supporting ML pipelines, including models developed in PyTorch. Strong Python and SQL skills. Deep understanding of data modeling (OLTP, OLAP, lakehouse) and architectural patterns. Experience with CI/CD pipelines and DevOps practices on AWS. Experience: Designing end-to-end data architectures across batch, streaming, and real-time use cases. Developing scalable data models, lakehouse structures, and metadata strategies. rchitecting ETL/ELT solutions using AWS Glue Jobs, Glue Data Catalog, Glue Studio, and Glue Workflows. Designing AWS architectures using S3, Lake Formation, Athena, Redshift, IAM, KMS, VPC, CloudWatch, and CloudTrail. Building data pipelines using Glue, Lambda, Step Functions, EMR, Athena, and S3. Implementing data quality, reliability, observability, and lineage solutions. Collaborating with ML teams to productionize PyTorch-based models. rchitecting feature stores, training pipelines, and large-scale ML workflows. Implementing governance policies, IAM roles, encryption standards, and compliance practices. Using AWS KMS, Secrets Manager, and Parameter Store for secret and encryption management. Leading technical teams and guiding cloud platform standards. Responsibilities: Design and implement modern data architectures aligned with business needs. Build and optimize batch, streaming, and real-time data pipelines in AWS. rchitect and manage ETL/ELT solutions using AWS Glue. Enable machine learning workflows including data prep, training, and inference pipelines. Define governance, security, and compliance frameworks across AWS data services. Lead architecture reviews and drive cost optimization strategies. Mentor data engineers and collaborate with ML teams and stakeholders. Evaluate and adopt scalable, emerging technologies within the AWS. ecosystem Should Have: Experience with EMR, Redshift, Kinesis, or Kafka. Knowledge of MLOps tools such as SageMaker, MLflow, and feature store platforms. Familiarity with Infrastructure-as-Code tools like Terraform or CloudFormation. Experience working in enterprise-scale and regulated environments. Skills: WS data services (Glue, S3, Athena, Lake Formation). Python, SQL, distributed data processing. DAG orchestration tools. Data modeling and data architecture. ML pipeline integration. Governance, security, and compliance frameworks. CI/CD and DevOps on AWS. Qualification and Education: Relevant degree in computer science, data engineering, or related field. Preferred Certifications: WS Certified Data Analytics - Specialty. WS Solutions Architect. Other AWS or data engineering certifications.

Created: 2026-03-04