AWS Data Engineer

We are looking for AWS Data Engineer for our client. Please check the details below and let me know if you are interested. Job Title: AWS Data Engineer Location: Remote – EST Time support Duration – 12+ Months The ideal candidate will have hands-on expertise in AWS-native data services, data lake architecture, ETL/ELT development, orchestration frameworks, and data governance best practices. Key Responsibilities1. Data Ingestion & Integration • Design and implement real-time and batch data ingestion pipelines using: • Amazon Kinesis (Data Streams and Data Firehose) • AWS Database Migration Service (DMS) for CDC and database replication • AWS AppFlow for SaaS data integration • Ingest structured and unstructured data from applications, databases, SaaS platforms, and event streams. • Ensure data quality, reliability, and performance of ingestion frameworks. 1. Data Lake & Storage Architecture • Build and manage scalable data lake architectures using Amazon S3 as the core storage layer. • Design optimized storage formats (Parquet, ORC, Avro, JSON, CSV) for performance and cost efficiency. • Implement enterprise data warehouse solutions using Amazon Redshift, including Redshift Serverless where appropriate. • Support operational and low-latency use cases leveraging Amazon DynamoDB. 1. Data Processing & Transformation • Develop scalable ETL/ELT pipelines using AWS Glue (Glue Jobs, Crawlers, Data Catalog). • Design and manage distributed processing workloads on Amazon EMR for complex Spark/Hadoop-based transformations. • Implement lightweight, event-driven transformations using AWS Lambda. • Optimize job performance, cost efficiency, and pipeline reliability. 1. Analytics & Query Enablement • Enable serverless analytics using Amazon Athena integrated with the Glue Data Catalog. • Implement hybrid data lake/warehouse architectures using Amazon Redshift Spectrum. • Support log analytics and search use cases using Amazon OpenSearch Service. Deliver optimized datasets to BI platforms and analytics teams. 1. Orchestration & Workflow Management • Develop and manage data workflows using: • AWS Step Functions • Amazon Managed Workflows for Apache Airflow (MWAA) • Build DAG-based pipeline orchestration for complex, multi-stage data processing environments. • Ensure fault tolerance, retry logic, dependency management, and operational visibility. 1. Data Governance, Security & Observability • Implement fine-grained data access controls using AWS Lake Formation. • Design secure IAM role structures using AWS Identity and Access Management (IAM). • Monitor pipelines and system health using Amazon CloudWatch. • Ensure compliance with enterprise security, audit, and data governance policies. 1. Business Intelligence & Data Consumption • Deliver curated datasets to analytics stakeholders using Amazon QuickSight. • Optimize data models for reporting, dashboards, and advanced analytics. • Partner with data scientists, analysts, and business teams to support analytical use cases. Required Qualifications • 3–8+ years of experience in data engineering or cloud data platform development. • Strong hands-on experience with AWS data services, particularly S3, Glue, Redshift, Athena, and Kinesis. • Experience designing scalable batch and/or streaming data pipelines. • Proficiency in SQL and distributed processing frameworks (e.g., Spark). • Experience with workflow orchestration tools such as Airflow (MWAA) or Step Functions. • Strong understanding of data modeling, partitioning strategies, and performance optimization. • Knowledge of IAM-based security and data governance controls. Preferred Qualifications • AWS certifications (e.g., AWS Certified Data Analytics – Specialty or Solutions Architect). • Experience supporting real-time streaming architectures. • Experience building enterprise-scale data lakes and hybrid warehouse architectures. • Familiarity with DevOps and CI/CD practices for infrastructure-as-code deployments.