Module Lead - AWS

We are looking forward to hire AWS Professionals in the following areas :

Job Descreption:

Role Overview

We are seeking an experienced AWS Data Architect with strong hands-on expertise in designing, building, and optimizing scalable data lake and analytics platforms on AWS. The ideal candidate will combine deep technical proficiency in AWS services with a strong understanding of data architecture principles, governance, and best practices for analytical enablement .

Required Skills :

• Proven experience architecting and implementing AWS data lake and analytics ecosystems.
• Hands-on expertise in AWS Glue, S3, Athena, Lambda, Redshift, and Lake Formation.
• Strong programming and ETL skills using PySpark / Python.
• Deep understanding of data modeling, data ingestion patterns, and schema evolution.
• Knowledge of data governance, security, and compliance frameworks in cloud environments.
• Familiarity with CI/CD, IaC, and DevOps tools (CDK, Terraform, GitHub Actions, CodePipeline).
• Experience with data quality frameworks (Deequ, Great Expectations).
• Strong problem-solving, analytical, and communication skills.

Preferred Qualifications

• At least 5-7 years of experience in data architecture and data modelling.

• AWS Certified Data Analytics - Specialty or AWS Certified Solutions Architect - Professional.

• Proven experience with database management systems and data warehousing.

• Strong understanding of data governance and data quality principles.

• Excellent problem-solving and analytical skills.

• Experience with data modelling tools and techniques.

Responsibilities

• Architect and Design: Define and implement scalable, secure, and cost-efficient AWS-based data lake architectures using S3, Glue, Athena, and Lake Formation.
• Data Engineering: Develop robust ETL/ELT pipelines using PySpark, Glue, and Lambda for batch and near-real-time data ingestion and transformation.
• Data Modeling: Design logical and physical data models, implement schema evolution, partitioning strategies, and efficient metadata management.
• Manage the complete data lifecycle from raw ingestion to curated and analytical layers ensuring data quality, lineage, and traceability.
• Integration: Enable seamless data consumption by downstream systems such as Redshift Spectrum, QuickSight, SageMaker, and other BI platforms.
• Enforce robust data governance, access control, and compliance through IAM, KMS, CloudTrail, and Lake Formation.
• Data Quality: Implement and automate data validation frameworks using tools such as Deequ or Great Expectations.
• Automation & DevOps: Build infrastructure as code (IaC) using AWS CDK or Terraform, and orchestrate CI/CD pipelines with GitHub Actions or AWS CodePipeline.
• Monitoring & Optimization: Set up observability, performance tuning, and cost optimization through CloudWatch, Glue job optimization, and Athena query tuning.

Our Hyperlearning workplace is grounded upon four principles

• Flexible work arrangements, Free spirit, and emotional positivity
• Agile self-determination, trust, transparency, and open collaboration
• All Support needed for the realization of business goals,
• Stable employment with a great atmosphere and ethical corporate culture