Data Architect
Project Role Description : Define the data requirements and structure for the application. Model and design the application data structure, storage and integration.
Must have skills : Data Architecture Principles
Good to have skills : NA
Minimum 7.5 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary: We are seeking a highly skilled Senior Data Engineer / Technical Architect – AWS to lead the design, development, and optimization of scalable data lake and ETL solutions in the AWS ecosystem. Roles & Responsibilities: • Lead the design, architecture, and development of large-scale ETL and data lake solutions using AWS Glue, S3, and related services. • Develop, monitor, and optimize ETL pipelines for multi-source data ingestion, transformation, and integration into centralized data lakes. • Create and manage the AWS Glue Data Catalog, ensuring consistent metadata management, schema evolution, and version control. • Implement data governance, quality checks, and security policies using AWS Lake Formation, IAM, and encryption best practices. • Collaborate with cross-functional teams — including data analysts, scientists, and business users — to translate business needs into scalable technical solutions. • Design and manage CI/CD pipelines for AWS ETL code deployment using GitHub, CloudWatch, and Glue workflows. • Optimize PySpark scripts and ETL jobs for performance, cost efficiency, and scalability using partitioning, parallelism, and compression techniques. • Support downstream analytics and BI platforms by delivering clean, curated, and query-optimized datasets. • Provide technical leadership and mentorship to junior data engineers, ensuring adherence to best practices in design, coding, and documentation. • Ensure data lineage, monitoring, and alerting via AWS CloudWatch and Glue consoles. Professional & Technical Skills: • AWS Data Engineering Services: Glue, S3, Athena, Redshift, Step Functions, Lambda, SNS, IAM, EventBridge, CloudWatch, Lake Formation, Iceberg, CLI. • Programming Languages: Python, PySpark, SQL. • ETL Development: Glue Jobs, Crawlers, Workflows, and Triggers for batch and near-real-time data processing. • Data Lake & Catalog Management: Schema evolution, metadata consistency, partitioning strategies, and cost optimization. • Data Quality & Governance: Validation frameworks, data profiling, lineage, and compliance with enterprise standards. • Integration: Redshift, Athena, QuickSight, and other downstream analytics/BI tools. • Automation & CI/CD: GitHub, Glue workflows, Step Functions, and CloudWatch for orchestration and deployment. • Big Data & IoT: Design and implementation of scalable big data and IoT-based data ingestion pipelines. • Databases: Relational and NoSQL databases, data modeling, and query optimization.
Hyderabad
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