AWS Glue Data Engineer

We are seeking a highly skilled AWS Data Engineer to design, develop, and optimize large-scale data pipelines and ETL workflows on AWS. The ideal candidate will have strong expertise in AWS cloud-native data services, data modeling, and pipeline orchestration, with hands-on experience building robust and scalable data solutions for enterprise environments., * Design and implement incremental and CDC (Change Data Capture) data pipelines using AWS Glue, DMS, and Iceberg to support near real-time analytics.

• Develop and maintain metadata-driven ETL frameworks to improve reusability, scalability, and operational efficiency across data platforms.
• Create and manage partitioning, compaction, and optimization strategies for Iceberg datasets to reduce query latency and storage costs.
• Build and orchestrate complex workflows using AWS Step Functions, EventBridge, Lambda, and Glue Workflows for automated data processing.
• Perform performance tuning and cost optimization of AWS Glue jobs by optimizing Spark configurations, worker types, partitioning, and job bookmarks.
• Implement CI/CD pipelines for data engineering solutions using AWS CodePipeline, CodeBuild, GitHub, Jenkins, or Terraform.
• Develop and maintain data lake architecture following AWS best practices, ensuring scalability, reliability, and governance.
• Automate data validation and reconciliation processes to ensure data accuracy, completeness, and consistency across multiple systems.
• Create and maintain Athena external tables, Iceberg catalogs, and Glue Data Catalog metadata for efficient data discovery and querying.
• Design and implement role-based access controls (RBAC), data masking, encryption, and audit mechanisms using Lake Formation and IAM policies.
• Support real-time and batch processing architectures integrating Kafka, Kinesis, PostgreSQL, S3, and Redshift.
• Monitor data pipelines using CloudWatch, SNS, AWS Glue Monitoring, and custom alerting mechanisms to ensure SLA compliance.
• Work closely with enterprise architecture and governance teams to establish data standards, retention policies, and compliance frameworks.
• Perform root cause analysis and resolve complex production issues involving Spark, Glue, Iceberg metadata, PostgreSQL connectivity, and permission models.
• Enable self-service analytics by creating curated gold, silver, and bronze data layers within enterprise data lakes.
• Manage schema evolution and version control for Iceberg datasets while maintaining backward compatibility for downstream consumers.
• Develop reusable PySpark utilities, frameworks, and common libraries to standardize data ingestion and transformation patterns.
• Participate in architecture reviews and recommend best practices for data lake modernization, cloud migration, and platform optimization initiatives.
• Implement data lineage, cataloging, and observability solutions to improve data trust, discoverability, and governance.
• Collaborate with DevOps and Infrastructure teams to provision and manage AWS resources using Terraform, CloudFormation, or Infrastructure as Code (IaC) methodologies

• 12+ years of experience in Data Engineering, with at least 5+ years designing and implementing AWS cloud-based data platforms and enterprise-scale data lakes.
• Strong hands-on expertise in AWS Glue, AWS DMS, Amazon S3, Amazon Redshift, Athena, Lambda, Step Functions, EventBridge, CloudWatch, SNS, and Glue Data Catalog.
• Advanced proficiency in PySpark, Spark, Python, and SQL, with experience building reusable frameworks, ETL/ELT pipelines, and high-volume data transformation solutions.
• Hands-on experience designing and implementing incremental and Change Data Capture (CDC) pipelines using AWS DMS, Glue, and related AWS services.
• Strong experience with Apache Iceberg including partitioning strategies, compaction, schema evolution, metadata management, performance optimization, and Iceberg catalog implementation.
• Extensive experience building and supporting enterprise Data Lake architectures using Bronze, Silver, and Gold data layers with strong focus on scalability, reliability, governance, and self-service analytics.
• Experience implementing and orchestrating complex workflows using AWS Step Functions, Glue Workflows, Lambda, and EventBridge.
• Strong knowledge of data modeling, including dimensional, star, snowflake, and lakehouse modeling techniques.
• Experience optimizing AWS Glue and Spark workloads, including partitioning, job bookmarks, worker sizing, Spark configuration tuning, and cost optimization.
• Hands-on experience with real-time and batch data processing architectures integrating Kafka, Kinesis, PostgreSQL, S3, and Redshift.
• Experience implementing data quality, reconciliation, observability, lineage, monitoring, and alerting frameworks across enterprise data platforms.
• Strong understanding of data security and governance, including Lake Formation, IAM, RBAC, encryption, masking, auditing, retention policies, and regulatory compliance requirements.
• Experience with CI/CD and Infrastructure as Code (IaC) using Terraform, CloudFormation, AWS CodePipeline, CodeBuild, GitHub, Jenkins, or similar technologies.
• Strong analytical, troubleshooting, and root cause analysis skills for resolving complex production issues across Spark, Glue, Iceberg, data pipelines, and cloud infrastructure.
• Experience working in Agile/Scrum environments and collaborating with architecture, governance, DevOps, and business stakeholders., * Experience in Financial Services, Wealth Management, Brokerage, Capital Markets, or BFSI domains, preferably supporting regulatory and governed data environments.
• AWS Certified Data Engineer - Associate certification required/preferred; additional AWS certifications in Analytics, Data Engineering, or Solutions Architecture are highly desirable.
• Hands-on experience with AWS Glue, PySpark, Apache Iceberg, Lake Formation, Kafka, Kinesis, and Amazon EMR/Spark in large-scale enterprise implementations.
• Experience building metadata-driven ingestion and ETL frameworks and platform accelerators for reusable data engineering patterns.
• Experience implementing data cataloging, lineage, observability, and governance solutions using enterprise data management tools.
• Familiarity with modern Lakehouse architectures, cloud migration initiatives, and data platform modernization programs.
• Experience with Terraform, CloudFormation, GitHub Actions, Jenkins, CodePipeline, and DevOps practices for enterprise data platforms.
• Master’s degree in Computer Science, Information Systems, Engineering, or related discipline preferred.

Education

• Bachelor’s degree in Computer Science, Information Technology, Engineering, or related field.

• Master’s degree preferred.