Data Engineer

Smart IT Frame LLC; Reston (VA); On-site; USD 110,000 - 180,000; Full time

Job summary

A leading technology firm is seeking an experienced AWS Data Engineer to design, build, and maintain scalable data pipelines. The ideal candidate will have strong skills in Python and PySpark, proven experience with a variety of AWS services, and solid SQL abilities. Responsibilities include building ETL pipelines, ensuring data quality, and collaborating with stakeholders. This full-time position is perfect for someone looking to leverage their AWS knowledge in a dynamic environment.

Qualifications

• Strong experience with Python and PySpark for large-scale data processing.
• Proven hands-on experience with AWS services: Lambda, SNS, SQS, Glue, Redshift, Step Functions.
• Solid SQL skills and familiarity with data modeling and query optimization.
• Experience with ETL best practices, data quality checks, and monitoring/alerting.
• Familiarity with version control (Git) and basic DevOps/CI-CD workflows.

Responsibilities

• Build and maintain ETL pipelines using Python and PySpark on AWS.
• Orchestrate workflows with AWS Step Functions and serverless components.
• Implement messaging and event-driven patterns using AWS SNS and SQS.
• Design and optimize data storage and querying in Amazon Redshift.
• Ensure data quality, monitoring, error handling and operational support for pipelines.
• Collaborate with data consumers and stakeholders to translate requirements into solutions.
• Contribute to CI/CD, infrastructure-as-code, and documentation for deployments.
• Contribute to CI/CD, infrastructure-as-code, and documentation for reproducible deployments.

Skills

Python

PySpark

AWS Lambda

AWS SNS

AWS SQS

AWS Glue

AWS Redshift

SQL

Version control (Git)

CI/CD workflows

Tools

AWS Lambda

AWS SNS

AWS SQS

AWS Glue

Redshift

Step Functions

Job description

Seeking an AWS Data Engineer to design, build, and maintain scalable data pipelines and ETL solutions using Python/Pyspark and AWS managed services to support analytics and data product needs.

Key Responsibilities

• Build and maintain ETL pipelines using Python and PySpark on AWS Glue and other compute platforms
• Orchestrate workflows with AWS Step Functions and serverless components (Lambda)
• Implement messaging and event-driven patterns using AWS SNS and SQS
• Design and optimize data storage and querying in Amazon Redshift
• Write performant SQL for data transformations, validation, and reporting
• Ensure data quality, monitoring, error handling and operational support for pipelines
• Collaborate with data consumers, engineers, and stakeholders to translate requirements into solutions
• Contribute to CI/CD, infrastructure-as-code, and documentation for reproducible deployments

Required Skills

• Strong experience with Python and PySpark for large-scale data processing
• Proven hands‑on experience with AWS services: Lambda, SNS, SQS, Glue, Redshift, Step Functions
• Solid SQL skills and familiarity with data modeling and query optimization
• Experience with ETL best practices, data quality checks, and monitoring/alerting
• Familiarity with version control (Git) and basic DevOps/CI‑CD workflows

Seniority level

• Mid‑Senior level

Employment type

• Full‑time

Job function

• Other

Industries

• Software Development and IT Services and IT Consulting