AWS Data Engineer Dallas, TX on W2

Job Title: AWS Data Engineer
Employment Type: W2 Contract Only
Location: Dallas, TX
Work Arrangement: Onsite
Experience: 10+ Years

Job Summary

We are seeking an experienced AWS Data Engineer with 10+ years of data engineering experience and strong expertise in AWS cloud data platforms. The ideal candidate will have hands-on experience building scalable data pipelines, ETL/ELT processes, data lakes, data warehouses, and cloud-based data solutions.

Key Responsibilities

• Design, develop, and maintain scalable AWS data pipelines.
• Build and optimize ETL/ELT pipelines for batch and real-time data processing.
• Develop data ingestion solutions from databases, APIs, files, and enterprise applications.
• Work extensively with AWS S3, Glue, Lambda, Redshift, EMR, Athena, Kinesis, and Step Functions.
• Develop scalable data processing applications using PySpark / Spark.
• Perform data transformation and processing using Python and SQL.
• Design data models and structures for analytics and reporting.
• Build and optimize Amazon Redshift data warehouse solutions.
• Implement data quality, validation, reconciliation, monitoring, and error-handling processes.
• Optimize pipelines for performance, scalability, reliability, and cost efficiency.
• Work with structured and semi-structured data including JSON, CSV, Parquet, and Avro.
• Implement AWS data lake / lakehouse architectures.
• Develop automated workflows and data orchestration processes.
• Integrate data from relational and NoSQL databases.
• Implement CI/CD practices for data engineering code and infrastructure.
• Collaborate with DevOps and cloud teams to automate deployments.
• Troubleshoot pipeline failures and perform root-cause analysis.
• Collaborate with data architects, analysts, application teams, and business stakeholders.
• Maintain technical documentation, data lineage, and engineering standards.

Required Skills

• 10+ years of experience in Data Engineering
• Strong hands-on experience with AWS
• Python, SQL, PySpark / Spark
• AWS Glue, S3, Redshift, Lambda, EMR, Athena, Kinesis, Step Functions
• ETL/ELT and data pipeline development
• Data Lake / Lakehouse architecture
• Data Warehousing and Data Modeling
• Batch and real-time data processing
• CI/CD and cloud deployment practices

Experience with structured and semi-structured data formats

AWS Cloud | Data Engineering | ETL | ELT | Data Pipelines | Data Ingestion | Data Transformation | Python | SQL | PySpark | Apache Spark | AWS S3 | AWS Glue | AWS Lambda | Amazon Redshift | Amazon EMR | Amazon Athena | Amazon Kinesis | AWS Step Functions | Data Lake | Data Lakehouse | Data Warehouse | Data Modeling | Batch Processing | Real-Time Data Processing | CI/CD | DevOps | Data Quality | Data Validation | Data Reconciliation | Data Monitoring | Performance Optimization | Cost Optimization | JSON | CSV | Parquet | Avro | Relational Databases | NoSQL Databases | Data Orchestration | Data Lineage | Root Cause Analysis